Accessibility is for Everyone


Accessibility is for everyone. I say that whenever an abled person finds a way that an accessibility feature benefits them. But that’s not all that it means. There are really three different meanings to that phrase:

  • Accessibility exists to make things accessible to everyone.
  • At some point, everyone has some kind of impairment which accessibility can help them with.
  • Changes that make things more accessible can be useful, convenient, or just plain fun, even for people who are 100% unimpaired.

Is this article for everyone?

This is a bare-bones outline of ways accessibility is for everyone, with a few lists of examples from my personal experience, and not much prose. This topic is fractal, though, and like a Koch Snowflake, even its outline could extend to infinite length. I’ve linked to more in-depth references where I knew of them, but tried not to go too far into detail on how to make things accessible. There are much better references for that — let me know of the ones you like in the comments.

I am not everyone

Although I do face mobility challenges in the physical world, as a software developer, I know the most about accessibility as it applies to computers. Within that, I have most experience with text-to-speech, so a lot of the examples relate to that. I welcome comments on aspects I missed. I am not an expert on accessibility, but I’d like to be.

The accessibility challenges that affect me the most are:

  • A lack of fluency in the language of the country I live in
  • Being short (This sounds harmless, but I once burnt my finger slightly because my microwave is mounted above my line of sight.)
  • Cerebral palsy spastic diplegia

That last thing does not actually affect how I use computers very much, but it is the reason I’ve had experience with modern computers from a young age.

Accessibility makes things accessible to everyone

Accessibility is for everyone — it allows everyone to use or take part in something, not just people with a certain range of abilities. This is the real goal of accessibility, and this alone is enough to justify improving accessibility. The later points in this article might help to convince people to allocate resources to accessibility, but always keep this goal in mind.

Ideally, everyone should be able to use a product without asking for special accommodations. If not, there should be a plan to accommodate those who ask, when possible. At the very least, nobody should be made to feel like they’re being too demanding just for asking for the same level of access other people get by default. Accessibility is not a feature — lack of accessibility is a bug.

Don’t make people ask

If some people have to ask questions when others don’t, the product is already less accessible to them — even if you can provide everything they ask for. This applies in a few scenarios:

  • Asking for help to use the product (e.g. help getting into a building, or using a app)
  • Asking for help accessing the accessibility accommodations. For example, asking for the key for an elevator, or needing someone else to configure the accessibility settings in software. Apple does a great job of this by asking about accessibility needs, with the relevant options turned on, during installation of macOS.
  • Asking about the accommodations available to find out if something is accessible to them before wasting time, spoons, or money on it. Make this information publicly available, e.g. on the website of your venue or event, or in your app’s description. Here’s a guide on writing good accessibility information.

Asking takes time and effort, and it can be difficult and embarrassing, whether because someone has to ask many times a day, or because they don’t usually need help and don’t like acknowledging when they do. 

In software, ‘making people ask’ is making them set up accessibility in your app when they’ve already configured the accessibility accommodations they need in the operating system. Use the system settings, rather than having your own settings for font size, dark mode, and so on. If the user has to find your extra settings before they can even use your app, there’s a good chance they won’t. Use system components as much as possible, and they’ll respect accessibility options you don’t even know about.

If they ask, have an answer

Perhaps you don’t have the resources to provide certain accommodations to everyone automatically, or it doesn’t make sense to. In that case:

  • make it clear what is available.
  • make asking for it as easy as possible (e.g. a checkbox or text field on a booking form, rather than instructions to call somebody)
  • make an effort to provide whatever it is to those who ask for it.

Assume the person really does need what they’re asking for — they know their situation better than you do.

If the answer is ‘no, sorry’, be compassionate about it

If you can’t make something accessible to a given group of people, don’t feel bad; we all have our limitations. But don’t make those people feel bad either — they have their limitations too, and they’re the ones missing out on something because of it. Remember that they’re only asking for the same thing everyone else gets automatically — they didn’t choose to need help just to annoy you.

If you simply didn’t think about their particular situation, talk with them about steps you could take. Don’t assume you know what they can or can’t do, or what will help them.

Everyone can be impaired

Accessibility is for everyone. But just like how even though all lives matter it is unfortunately still necessary to remind some people that black lives do, to achieve accessibility for everyone, we need to focus on the people who don’t get it by default. So who are they?

Apple’s human interface guidelines for accessibility say this better than I could:

Approximately one in seven people worldwide have a disability or impairment that affects the way they interact with the world and their devices. People can experience impairments at any age, for any duration, and at varying levels of severity. Situational impairments — temporary conditions such as driving a car, hiking on a bright day, or studying in a quiet library — can affect the way almost everyone interacts with their devices at various times.

Almost everyone.

This section will mostly focus on accessibility of devices such as computers, tablets, and phones. It’s what I know best, and malfunctioning hardware can be another source of impairment. Even if you don’t consider yourself disabled, if you haven’t looked through the accessibility settings of your devices yet, do so — you’re sure to find something that will be useful to you in some situations. I’ll list some ways accessibility can help with hardware issues and other situational impairments below.

Apple defines four main kinds of impairment:

Vision

There’s a big gap between someone with 20/20 full-colour vision in a well-lit room looking at an appropriately-sized, undamaged screen, and someone with no vision whatsoever. There’s even a big gap between someone who is legally blind and someone with no vision whatsoever. Whenever we are not at the most abled end of that spectrum, visual accessibility tools can help.

Here are some situations where I’ve used Vision accessibility settings to overcome purely situational impairments:

  • When sharing a screen over a videoconference or to a projector, use screen zoom, large cursor or font sizes, and Hover Text (on macOS.) This makes things visible to the audience regardless of the size of their videoconference window or how far they are from the projector screen.
  • When an internet connection is slow, or you don’t want to load potential tracking images in emails, image descriptions (alt text) let you know what you’re missing.
  • When a monitor doesn’t work until the necessary software is installed and configured, use a screenreader to get through the setup. I’ve done this on a Mac, after looking up how to use VoiceOver on another device.

Hearing

There’s a big gap between someone with perfect hearing and auditory processing using good speakers at a reasonable volume in an otherwise-quiet room, and someone who hears nothing at all. There’s even a big gap between someone who is Deaf and someone who hears nothing at all. Whenever we are not at the most abled end of that spectrum, hearing accessibility tools can help.

Here are some situations where I’ve used Hearing accessibility settings when the environment or hardware was the only barrier:

  • When one speaker is faulty, change the panning settings to only play in the working speaker, and turn on ‘Play stereo audio as mono’.
  • When a room is noisy or you don’t want to disturb others with sound, use closed captions.

Physical and Motor

There’s a big gap between someone with a full range of controlled, pain-free movement using a perfectly-functioning device, in an environment tailored to their body size, and someone who can only voluntarily twitch a single cheek muscle (sorry, but we can’t all be Stephen Hawking.) Whenever we are not at the most abled end of that spectrum, motor accessibility tools can help.

Here are some situations where you can use Physical and Motor accessibility to overcome purely situational impairments:

  • When a physical button on an iPhone doesn’t work reliably, use Back Tap, Custom Gestures, or the AssistiveTouch button to take over its function.
  • When you’re carrying something bulky, use an elevator. I’ve shared elevators with people who have strollers, small dogs, bicycles, suitcases, large purchases, and disabilities. I’ve also been yelled at by someone who didn’t think I should use an elevator, because unlike him, I had no suitcase. Don’t be that person.

Literacy and Learning

This one is also called Cognitive. There’s a big gap between an alert, literate, neurotypical adult of average intelligence with knowledge of the relevant environment and language, and… perhaps you’ve thought of a disliked public figure you’d claim is on the other end of this spectrum. There’s even a big gap between that person and the other end of this spectrum, and people in that gap don’t deserve to be compared to whomever you dislike. Whenever we are not at the most abled end of that spectrum, cognitive accessibility considerations can help.

Here are some situations where I’ve used accessibility when the environment was the only barrier to literacy:

  • When watching or listening to content in a language you know but are not fluent in, use closed captions or transcripts to help you work out what the words are, and find out the spelling to look them up.
  • When reading in a language you know but are not fluent in, use text-to-speech in that language to find out how the words are pronounced.
  • When consuming content in a language you don’t know, use subtitles or translations.

Accessibility features benefit abled people

Sometimes it’s hard to say what was created for the sake of accessibility and what wasn’t. Sometimes products for the general public bring in the funding needed to improve assistive technologies. Here are some widely-used things which have an accessibility aspect:

  • The Segway was based on self-balancing technology originally developed for wheelchairs. Segways and the like are still used by some people as mobility devices, even if they are not always recognised as such.
  • Voice assistants such as Siri rely on speech recognition and speech synthesis technology that has applications in all four domains of accessibility mentioned above.
  • Light or Dark mode may be a style choice for one person and an essential visual accessibility tool for another.

Other technology is more strongly associated with accessibility. Even when your body, your devices, or your environment don’t present any relevant impairment, there are still ways that these things can be useful, convenient, or just plain fun.

Useful

Some accessibility accommodations let abled people do things they couldn’t do otherwise.

  • Transcripts, closed captions, and image descriptions are easily searchable.
  • I’ve used text-to-speech APIs to generate the initial rhyme database for my rhyming dictionary, rhyme.science
  • I’ve used text-to-speech to find out how words are pronounced in different languages and accents.
  • Menstruators can use handbasins in accessible restroom stalls to rinse out menstrual cups in privacy. (This is not an argument for using accessible stalls when you don’t need them — it’s an argument for more handbasins installed in stalls!)

Convenient

Some accessibility tech lets abled people do things they would be able to do without it, but in a more convenient way.

  • People who don’t like switching between keyboard and mouse can enable full keyboard access on macOS to tab through all controls. They can also use keyboard shortcuts.
  • People who don’t want to watch an entire video to find out a piece of information can quickly skim a transcript.
  • I’ve used speak announcements on my Mac for decades. If my Mac announces something while I’m on the other side of the room, I know whether I need to get up and do something about it.
  • Meeting attendees could edit automatic transcripts from videoconferencing software (e.g. Live Transcription in Zoom) to make meeting minutes.
  • I’ve used text-to-speech on macOS and iOS to speak the names of emojis when I wasn’t sure what they were.
  • Pre-chopped produce and other prepared foods save time even for people who have the dexterity and executive function to prepare them themselves.

Fun

Some accessibility tech lets us do things that are not exactly useful, but a lot of fun.

  • Hosts of the Lingthusiasm podcast, Lauren Gawne and Gretchen McCulloch, along with Janelle Shane, fed transcripts of their podcasts into an artificial intelligence to generate a quirky script for a new episode, and then recorded that script.
  • I’ve used text-to-speech to sing songs I wrote that I was too shy to sing myself.
  • I’ve used text-to-speech APIs to detect haiku in any text.
  • Automated captions of video conferencing software and videos make amusing mistakes that can make any virtual party more fun. Once you finish laughing, make sure anyone who needed the captions knows what was really said. 
  • I may have used the ’say’ command on a server through an ssh connection to surprise and confuse co-workers in another room. 😏
  • I find stairs much more accessible if they have a handrail. You might find it much more fun to slide down the balustrade. 😁

Advocating accessibility is for everyone

I hope you’ve learnt something about how or why to improve accessibility, or found out ways accessibility can improve your own life. I’d like to learn something too, so put your own ideas or resources in the comments!

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NiceWriter: Artificially sweeten your text


Hello, pure world! 🥰

I’m a reputable app for distinguished iOS that puts positive adjectives before innocent nouns. My magical twin, NastyWriter, likes to add venerable insults to badass text, but I’d rather spread some peachy love. We’re not amusing enemies; rather, we’re complementary apps… it’s just that I’m also complimentary. 

Check me out on the tender App Store! I’m complimentary, supported by elegant ads, which you can remove with an in-app purchase. I hope I can make your finest day even better, and your mighty love notes sweeter. 

Lots of joyous love! 😊

complimentary adjectives by NiceWriter
NiceWriter introducing itself on Twitter

A few years ago I noticed a linguistic habit of Twitter user Donald Trump, and decided to emulate it by writing an app that automatically adds insults before nouns — NastyWriter. But he’s not on Twitter any more, and Valentine’s Day is coming up, so it’s time to make things nicer instead.

My new iOS app, NiceWriter, automatically adds positive adjectives, highlighted in pink, before the nouns in any text entered. Most features are the same as in NastyWriter:

  • You can use the contextual menu or the toolbar to change or remove any adjectives that don’t fit the context.
  • You can share the sweetened text as an image similar to the one in this post.
  • You can set up the ‘Give Me a Compliment’ Siri Shortcut to ask for a random compliment at any time, or create a shortcut to add compliments to text you’ve entered previously. You can even use the Niceify shortcut in the Shortcuts app to add compliments to text that comes from another Siri action.
  • If you copy and paste text between NiceWriter and NastyWriter, the app you paste into will replace the automatically-generated adjectives with its own, and remember which nouns you removed the adjectives from.

The app is free to download, and will show ads unless you buy an in-app purchase to remove them. I’ve made NiceWriter available to run on M1 Macs as well, though I don’t have one to test it on, so I can’t guarantee it will work well.

I’ll post occasional Niceified text on the NastyWriter Tumblr, and the @NiceWriterApp Twitter.

NastyWriter 2.1

In the process of creating NiceWriter, I made a few improvements to NastyWriter — notably adding input and output parameters to its Siri Shortcut so you can set up a workflow to nastify the results of other Siri Shortcuts, and then pass them on to other actions. I also added four new insults, and fixed a few bugs. All of these changes are in NastyWriter 2.1.

That’s all you really need to know, but for more details on how I chose the adjectives for NiceWriter and what I plan to do next, read on.

Read the rest of this entry »

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Top 35 Adjectives Twitter user @realdonaldtrump uses before nouns


Edit: As of 8 January, 2021, @realdonaldtrump is no longer a Twitter user, but he was at the time of this post.

Version 2.0.1 of my iOS app NastyWriter has 184 different insults (plus two extra special secret non-insults that appear rarely for people who’ve paid to remove ads 🤫) which it can automatically add before nouns in the text you enter. “But Angela,” I hear you not asking, “you’re so incredibly nice! How could you possibly come up with 184 distinct insults?” and I have to admit, while I’ve been known to rap on occasion, I have not in fact been studying the Art of the Diss — I have a secret source. (This is a bonus joke for people with non-rhotic accents.)

My secret source is the Trump Twitter Archive. Since NastyWriter is all about adding gratuitous insults immediately before nouns, which Twitter user @realdonaldtrump is such a dab hand at, I got almost all of the insults from there. But I couldn’t stand to read it all myself, so I wrote a Mac app to go through all of the tweets and find every word that seemed to be an adjective immediately before a noun. I used NSLinguisticTagger, because the new Natural Language framework did not exist when I first wrote it.

Natural language processing is not 100% accurate, because language is complicated — indeed, the app thought ‘RT’, ‘bit.ly’, and a lot of twitter @usernames (most commonly @ApprenticeNBC) and hashtags were adjectives, and the usernames and hashtags were indeed used as adjectives (usually noun adjuncts) e.g. in ‘@USDOT funding’. One surprising supposed adjective was ‘gsfsgh2kpc’, which was in a shortened URL mentioned 16 times, to a site which Amazon CloudFront blocks access to from my country.

For each purported adjective the app found, I had a look at how it was used before adding it to NastyWriter’s insult collection. Was it really an adjective used before a noun? Was it used as an insult? Was it gratuitous? Were there any other words it was commonly paired with, making a more complex insult such as ‘totally conflicted and discredited’, or ‘frumpy and very dumb’? Was it often in allcaps or otherwise capitalised in a specific way?

But let’s say we don’t care too much about that and just want to know roughly which adjectives he used the most. Can you guess which is the most common adjective found before a noun? I’ll give you a hint: he uses it a lot in other parts of sentences too. Here are the top 35 as of 6 November 2020:

  1. ‘great’ appears 4402 times
  2. ‘big’ appears 1351 times
  3. ‘good’ appears 1105 times
  4. ‘new’ appears 1034 times
  5. ‘many’ appears 980 times
  6. ‘last’ appears 809 times
  7. ‘best’ appears 724 times
  8. ‘other’ appears 719 times
  9. ‘fake’ appears 686 times
  10. ‘American’ appears 592 times
  11. ‘real’ appears 510 times
  12. ‘total’ appears 509 times
  13. ‘bad’ appears 466 times
  14. ‘first’ appears 438 times
  15. ‘next’ appears 407 times
  16. ‘wonderful’ appears 375 times
  17. ‘amazing’ appears 354 times
  18. ‘only’ appears 325 times
  19. ‘political’ appears 310 times
  20. ‘beautiful’ appears 298 times
  21. ‘fantastic’ appears 279 times
  22. ‘tremendous’ appears 270 times
  23. ‘massive’ appears 268 times
  24. ‘illegal’ appears 254 times
  25. ‘incredible’ appears 254 times
  26. ‘nice’ appears 251 times
  27. ‘strong’ appears 250 times
  28. ‘greatest’ appears 248 times
  29. ‘true’ appears 247 times
  30. ‘major’ appears 243 times
  31. ‘same’ appears 236 times
  32. ‘terrible’ appears 231 times
  33. ‘presidential’ appears 221 times
  34. ‘much’ appears 217 times
  35. ‘long’ appears 215 times

So as you can see, he doesn’t only insult. The first negative word, ‘fake’, is only the ninth most common, though more common than its antonyms ‘real’ and ‘true’, if they’re taken separately (‘false’ is in 72nd position, with 102 uses before nouns, while ‘genuine’ has only four uses.) And ‘illegal’ only slightly outdoes ‘nice’.

He also talks about American things a lot, which is not surprising given his location. ‘Russian’ comes in 111st place, with 62 uses, so about a tenth as many as ‘American’. As far as country adjectives go, ‘Iranian’ is next with 40 uses before nouns, then ‘Mexican’ with 39, and ‘Chinese’ with 37. ‘Islamic’ has 33. ‘Jewish’ and ‘White’ each have 27 uses as adjectives before nouns, though the latter is almost always describing a house rather than people. The next unequivocally racial (i.e. referring to a group of people rather than a specific region) adjective is ‘Hispanic’, with 25. I’m not an expert on what’s unequivocally racial, but I can tell you that ‘racial’ itself has nine adjectival uses before nouns, and ‘racist’ has three.

But Angela,” I hear you not asking, “why are you showing us a list of words and numbers? Didn’t you just make an audiovisual word cloud generator a few months ago?” and the answer is, yes, indeed, I did make a word cloud generator that makes visual and audio word clouds, So here is an audiovisual word cloud of all the adjectives found at least twice before nouns in tweets by @realdonaldtrump in The Trump Twitter Archive, with Twitter usernames filtered out even if they are used as adjectives. More common words are larger and louder. Words are panned left or right so they can be more easily distinguished, so this is best heard in stereo.

There are some nouns in there, but they are only counted when used as attributive nouns to modify other nouns, e.g. ‘NATO countries’, or ‘ObamaCare website’.

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NastyWriter 2.0.1


I suggest adding a ‘Help, I read the news!’ shortcut which responds with a nastified version of ‘The news is just a stream of bits wafting through you with no effect, like neutrinos through the Earth. Go look at pictures of cats to improve your life.’

I came upon a secret stash of free time, so I finally put finishing touches on the Siri Shortcuts I’d added to NastyWriter, made the app work properly in Dark Mode, added the latest gratuitous insults harvested from Twitter (I’ll write another post about how I did that), and released it. Then somebody pointed out something that still didn’t work in Dark Mode, so I fixed that and a few related things, and released it again. Thus NastyWriter’s version number (2.0.1) is the reverse of what it was before (1.0.2.)

The obstructionist news is just an overrated stream of angry and conflicted bits wafting through you with no unreliable effect, like baseless neutrinos through the very misguided Earth. Go look at filthy pictures of rigged cats to improve your bird-killing life.
A response to the ‘Oh no, I read the news!’ shortcut added in the previous screenshot.

I added Siri Shortcuts to NastyWriter soon after iOS 12 came out, just to learn a bit about them. You can add a shortcut with whatever text you’ve entered, and then run the shortcut whenever you like to get a freshly-nastified version of the same text.

There’s also a ‘Give me an insult’ shortcut (which you can find in the Shortcuts app) which just gives a random insult, surrounded by unpleasant emoji.

As I added these soon after iOS 12 came out, they don’t support parameters, which are new in iOS 13. I may work on that next, so you’ll be able to nastify text on the fly, or nastify the output from another shortcut as part of a longer workflow.

A Siri response saying '😡phony👎'
A result from the ‘Give me an insult’ shortcut

Since Tom Lehrer recently released all his music and lyrics into the public domain, I took this opportunity to update the screenshots of NastyWriter in the App Store to show Tom Lehrer’s song ‘She’s My Girl’ where they had previously shown Shakespeare’s Sonnet 18. You can read a full nastification of this on the NastyWriter tumblr.

I came upon a secret stash of free time, so I finally put finishing touches on the lightweight Siri Shortcuts I'd added to rigged NastyWriter, made the untruthful app work properly in phony and dishonest Dark Mode, added the latest gratuitous insults harvested from made up Twitter (I'll write another noisy post about how I did that), and released it. Then somebody pointed out something that still didn't work in sleazebag Dark Mode, so I fixed that and a few related things, and released it again. Thus NastyWriter's secretly dumped version number (2.0.1) is the crazed, crying reverse of what it was before (1.0.2.)

I added noisy Siri Shortcuts to NastyWriter soon after dachshund‑legged iOS 12 came out, just to learn a very misguided bit about them. You can add an outdated, broken shortcut with whatever bogus text you've entered, and then run the bad shortcut whenever you like to get a freshly-nastified version of the same text. 

There's also a 'Give me an extraordinarily low IQ insult' shortcut (which you can find in the frumpy and very dumb Shortcuts app) which just gives a random insult, surrounded by unpleasant emoji.

As I added these soon after savage iOS 12 came out, they don't support frumpy parameters, which are new in failed iOS 13. I may work on that next, so you'll be able to nastify crazed, crying text on the noisy fly, or nastify the depraved output from another crazy shortcut as VERY weak part of a longer workflow.

Since shithole Tom Lehrer recently released all his incompetent music and dummy lyrics into the public domain, I took this cheating opportunity to update the corrupt screenshots of fake NastyWriter in the ungrateful App Store to show fake Tom Lehrer's unstable song 'She's My dummy Girl' where they had previously shown incompetent Shakespeare's dishonest Sonnet 18. You can read a full nastification of this on the NastyWriter tumblr.
A nastified version of this post, in Dark Mode

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That time Steve Wozniak taught me to Segway and then played Tetris and pranks through a concert


A few weeks ago I posted a video of myself talking about the time Steve Wozniak gave me a laptop, and I said:

A few years later, I met Woz, had pizza and learnt to Segway with him, and watched him play Tetris and pranks all through a concert of The Dead, but that will probably be a different 18-minute video.

Well, last week I indeed recorded an 18-minute video about the time I met Woz; the raw video was coincidentally imported into Photos at the same minute of the day as the previous one, and was one second longer than it.

The final video, with turning the camera on and off trimmed out, is two seconds longer than the previous one.

The background is a little blurry, but in the first take the entire picture was blurry, so in comparison, a little artful background blur is fine.

The short version: I met a friend of Woz by complaining by email that the lights were turned off in Woz’s office, and then met that friend in San Francisco when I went there for WWDC 2004. We met Woz, who had flashing lights in his teeth, at a pizza restaurant, and then went to a concert, where we rode Segways and Woz confused people by flashing tooth lights and lasers at them while playing Tetris.

Here’s a playlist with both of my Woz stories. Perhaps this will be the start of a series of 18-minute videos about my ridiculous life, or perhaps not. I don’t have any more Woz stories, but I do have more stories.

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That time Steve Wozniak bought me a laptop


People seem to enjoy hearing this story, and Woz’s 70th birthday seems like a good occasion to tell it to more people. I in a lot of details of varying relevance (and was looking down at notes on my iPad a bit to keep track of them), because it my video and I may as well tell it my own way. But if you don’t have eighteen minutes to spare, there’s a short version in the next paragraph (to avoid spoilers.)

The short version: My then-boyfriend left my PowerBook in a phone booth, the PowerBook was held for ransom and not recovered, and meanwhile my sister emailed the Woz (who knew of me from having called me on my birthday half a year earlier) and offered to buy me a replacement.

A few years later, I met Woz, had pizza and learnt to Segway with him, and watched him play Tetris and pranks all through a concert of The Dead, but that will probably be a different 18-minute video.

If you think my life is ridiculous, well, you’re right, but also, you should see Steve Wozniak’s life! (His autobiography, iWoz, would be a great book to read to a cool kid at bedtime.) And check out the events, challenges, and fundraising going on at wozbday.com.

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Audio Word Clouds


For my comprehensive channel trailer, I created a word cloud of the words used in titles and descriptions of the videos uploaded each month. Word clouds have been around for a while now, so that’s nothing unusual. For the soundtrack, I wanted to make audio versions of these word clouds using text-to-speech, with the most common words being spoken louder. This way people with either hearing or vision impairments would have a somewhat similar experience of the trailer, and people with no such impairments would have the same surplus of information blasted at them in two ways.

I checked to see if anyone had made audio word clouds before, and found Audio Cloud: Creation and Rendering, which makes me wonder if I should write an academic paper about my audio word clouds. That paper describes an audio word cloud created from audio recordings using speech-to-text, while I wanted to create one from text using text-to-speech. I was mainly interested in any insights into the number of words we could perceive at once at various volumes or voices. In the end, I just tried a few things and used my own perception and that of a few friends to decide what worked. Did it work? You tell me.

Part of the System Voice menu in the Speech section of the Accessibility panel of the macOS Catalina System Preferences

Voices

There’s a huge variety of English voices available on macOS, with accents from Australia, India, Ireland, Scotland, South Africa, the United Kingdom, and the United States, and I’ve installed most of them. I excluded the voices whose speaking speed can’t be changed, such as Good News, and a few novelty voices, such as Bubbles, which aren’t comprehensible enough when there’s a lot of noise from other voices. I ended up with 30 usable voices. I increased the volume of a few which were harder to understand when quiet.

I wondered whether it might work best with only one or a few voices or accents in each cloud, analogous to the single font in each visual word cloud. That way people would have a little time to adapt to understand those specific voices rather than struggling with an unfamiliar voice or accent with each word. On the other hand, maybe it would be better to have as many voices as possible in each word cloud so that people could distinguish between words spoken simultaneously by voice, just as we do in real life. In the end I chose the voice for each word randomly, and never got around to trying the fewer-distinct-voices version. Being already familiar with many of these voices, I’m not sure I would have been a good judge of whether that made it easier to get used to them.

Arranging the words

It turns out making an audio word cloud is simpler than making a visual one. There’s only one dimension in an audio word cloud — time. Volume could be thought of as sort of a second dimension, as my code would search through the time span for a free rectangle of the right duration with enough free volume. I later wrote an AppleScript to create ‘visual audio word clouds’ in OmniGraffle showing how the words fit into a time/volume rectangle.  I’ve thus illustrated this post with a visual word cloud of this post, and a few audio word clouds and visual audio word clouds of this post with various settings.

A visual representation of an audio word cloud of an early version of this post, with the same hubbub factor as was used in the video. The horizontal axis represents time, and the vertical axis represents volume. Rectangles in blue with the darker gradient to the right represent words panned to the right, while those in red with the darker gradient to the left represent words panned to the left.

However, words in an audio word cloud can’t be oriented vertically as they can in a visual word cloud, nor can there really be ‘vertical’ space between two words, so it was only necessary to search along one dimension for a suitable space. I limited the word clouds to five seconds, and discarded any words that wouldn’t fit in that time, since it’s a lot easier to display 301032 words somewhat understandably in nine minutes than it is to speak them. I used the most common (and therefore louder) words first, sorted by length, and stopped filling the audio word cloud once I reached a word that would no longer fit. It would sometimes still be possible to fit a shorter, less common word in that cloud, but I didn’t want to include words much less common than the words I had to exclude.

I set a preferred volume for each word based on its frequency (with a given minimum and maximum volume so I wouldn’t end up with a hundred extremely quiet words spoken at once) and decided on a maximum total volume allowed at any given point. I didn’t particularly take into account the logarithmic nature of sound perception. I then found a time in the word cloud where the word would fit at its preferred volume when spoken by the randomly-chosen voice. If it didn’t fit, I would see if there was room to put it at a lower volume. If not, I’d look for places it could fit by increasing the speaking speed (up to a given maximum) and if there was still nowhere, I’d increase the speaking speed and decrease the volume at once. I’d prioritise reducing the volume over increasing the speed, to keep it understandable to people not used to VoiceOver-level speaking speeds. Because of the one-and-a-bit dimensionality of the audio word cloud, it was easy to determine how much to decrease the volume and/or increase the speed to fill any gap exactly. However, I was still left with gaps too short to fit any word at an understandable speed, and slivers of remaining volume smaller than my per-word minimum.

A visual representation of an audio word cloud of this post, with a hubbub factor that could allow two additional words to be spoken at the same time as the others.

I experimented with different minimum and maximum word volumes, and maximum total volumes, which all affected how many voices might speak at once (the ‘hubbub level’, as I call it). Quite late in the game, I realised I could have some voices in the right ear and some in the left, which makes it easier to distinguish them. In theory, each word could be coming from a random location around the listener, but I kept to left and right — in fact, I generated separate left and right tracks and adjusted the panning in Final Cut Pro. Rather than changing the logic to have two separate channels to search for audio space in, I simply made my app alternate between left and right when creating the final tracks. By doing this, I could increase the total hubbub level while keeping many of the words understandable. However, the longer it went on for, the more taxing it was to listen to, so I decided to keep the hubbub level fairly low.

The algorithm is deterministic, but since voices are chosen randomly, and different voices take different amounts of time to speak the same words even at the same number of words per minute, the audio word clouds created from the same text can differ considerably. Once I’d decided on the hubbub level, I got my app to create a random one for each month, then regenerated any where I thought certain words were too difficult to understand.

Capitalisation

The visual word cloud from December 2019, with both ‘Competition’ and the lowercase ‘competition’ featured prominently

In my visual word clouds, I kept the algorithm case-sensitive, so that a word with the same spelling but different capitalisation would be counted as a separate word, and displayed twice. There are arguments for keeping it like this, and arguments to collapse capitalisations into the same word — but which capitalisation of it? My main reason for keeping the case-sensitivity was so that the word cloud of Joey singing the entries to our MathsJam Competition Competition competition would have the word ‘competition’ in it twice.

Sometimes these really are separate words with different meanings (e.g. US and us, apple and Apple, polish and Polish, together and ToGetHer) and sometimes they’re not. Sometimes these two words with different meanings are pronounced the same way, other times they’re not. But at least in a visual word cloud, the viewer always has a way of understanding why the same word appears twice. For the audio word cloud, I decided to treat different capitalisations as the same word, but as I’ve mentioned, capitalisation does matter in the pronunciation, so I needed to be careful about which capitalisation of each word to send to the text-to-speech engine. Most voices pronounce ‘JoCo’ (short for Jonathan Coulton, pronounced with the same vowels as ‘go-go’) correctly, but would pronounce ‘joco’ or ‘Joco’ as ‘jocko’, with a different vowel in the first syllable. I ended up counting any words with non-initial capitals (e.g. JoCo, US) as separate words, but treating title-case words (with only the initial letter capitalised) as the same as all-lowercase, and pronouncing them in title-case so I wouldn’t risk mispronouncing names.

Further work

A really smart version of this would get the pronunciation of each word in context (the same way my rhyming dictionary rhyme.science finds rhymes for the different pronunciations of homographs, e.g. bow), group them by how they were pronounced, and make a word cloud of words grouped entirely by pronunciation rather than spelling, so ‘polish’ and ‘Polish’ would appear separately but there would be no danger of, say ‘rain’ and ‘reign’ both appearing in the audio word cloud and sounding like duplicates. However, which words are actually pronounced the same depend on the accent (e.g. whether ‘cot’ and ‘caught’ sound the same) and text normalisation of the voice — you might have noticed that some of the audio word clouds in the trailer have ‘aye-aye’ while others have ‘two’ for the Roman numeral ‘II’.

Similarly, a really smart visual word cloud would use natural language processing to separate out different meanings of homographs (e.g. bow🎀, bow🏹, bow🚢, and bow🙇🏻‍♀️) and display them in some way that made it obvious which was which, e.g. by using different symbols, fonts, styles, colours for different parts of speech. It could also recognise names and keep multi-word names together, count words with the same lemma as the same, and cluster words by semantic similarity, thus putting ‘Zoe Keating’ near ‘cello’, and ‘Zoe Gray’ near ‘Brian Gray’ and far away from ‘Blue’. Perhaps I’ll work on that next.

A visual word cloud of this blog post about audio word clouds, superimposed on a visual representation of an audio word cloud of this blog post about audio word clouds.

I’ve recently been updated to a new WordPress editor whose ‘preview’ function gives a ‘page not found’ error, so I’m just going to publish this and hope it looks okay. If you’re here early enough to see that it doesn’t, thanks for being so enthusiastic!

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How to fit 301032 words into nine minutes


A few months ago I wrote an app to download my YouTube metadata, and I blogged some statistics about it and some haiku I found in my video titles and descriptions. I also created a few word clouds from the titles and descriptions. In that post, I said:

Next perhaps I’ll make word clouds of my YouTube descriptions from various time periods, to show what I was uploading at the time. […] Eventually, some of the content I create from my YouTube metadata will make it into a YouTube video of its own — perhaps finally a real channel trailer. 

Me, two and a third months ago

TL;DR: I made a channel trailer of audiovisual word clouds showing each month of uploads:

It seemed like the only way to do justice to the number and variety of videos I’ve uploaded over the past thirteen years. My channel doesn’t exactly have a content strategy. This is best watched on a large screen with stereo sound, but there is no way you will catch everything anyway. Prepare to be overwhelmed.

Now for the ‘too long; don’t feel obliged to read’ part on how I did it. I’ve uploaded videos in 107 distinct months, so creating a word cloud for each month using wordclouds.com seemed tedious and slow. I looked into web APIs for creating word clouds automatically, and added the code to my app to call them, but then I realised I’d have to sign up for an account, including a payment method, and once I ran out of free word clouds I’d be paying a couple of cents each. That could easily add up to $5 or more if I wanted to try different settings! So obviously I would need to spend many hours programming to avoid that expense.

I have a well-deserved reputation for being something of a gadget freak, and am rarely happier than when spending an entire day programming my computer to perform automatically a task that it would otherwise take me a good ten seconds to do by hand. Ten seconds, I tell myself, is ten seconds. Time is valuable and ten seconds’ worth of it is well worth the investment of a day’s happy activity working out a way of saving it.

Douglas Adams in ‘Last chance to see…’

I searched for free word cloud code in Swift, downloaded the first one I found, and then it was a simple matter of changing it to work on macOS instead of iOS, fixing some alignment issues, getting it to create an image instead of arranging text labels, adding some code to count word frequencies and exclude common English words, giving it colour schemes, background images, and the ability to show smaller words inside characters of other words, getting it to work in 1116 different fonts, export a copy of the cloud to disk at various points during the progress, and also create a straightforward text rendering using the same colour scheme as a word cloud for the intro… before I knew it, I had an app that would automatically create a word cloud from the titles and descriptions of each month’s public uploads, shown over the thumbnail of the most-viewed video from that month, in colour schemes chosen randomly from the ones I’d created in the app, and a different font for each month. I’m not going to submit a pull request; the code is essentially unrecognisable now.

In case any of the thumbnails spark your curiosity, or you just think the trailer was too short and you’d rather watch 107 full videos to get an idea of my channel, here is a playlist of all the videos whose thumbnails are shown in this video:

It’s a mixture of super-popular videos and videos which didn’t have much competition in a given month.

Of course, I needed a soundtrack for my trailer. Music wouldn’t do, because that would reduce my channel trailer to a mere song for anyone who couldn’t see it well. So I wrote some code to make an audio version of each word cloud (or however much of it could fit into five seconds without too many overlapping voices) using the many text-to-speech voices in macOS, with the most common words being spoken louder. I’ll write a separate post about that; I started writing it up here and it got too long.

The handwritten thank you notes at the end were mostly from members of the JoCo Cruise postcard trading club, although one came with a pandemic care package from my current employer. I have regaled people there with various ridiculous stories about my life, and shown them my channel. You’re all most welcome; it’s been fun rewatching the concert videos myself while preparing to upload, and it’s always great to know other people enjoy them too.

I put all the images and sounds together into a video using Final Cut Pro 10.4.8. This was all done on my mid-2014 Retina 15-inch MacBook Pro, Sneuf.

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Unintentional Haiku in my YouTube Video Descriptions


Since I wrote a little app to download much of my YouTube metadata, it was obvious that I needed to feed it through another little app I wrote: Haiku Detector. So I did. In all of my public YouTube descriptions put together, with URLs removed, there are 26 172 sentences, and 436 detected haiku.

As is usually the case, a few of these ‘haiku’ were not really haiku because the Mac speech synthesis pronounces them wrong, and thus Haiku Detector counts their syllables incorrectly. A few more involved sentences which no longer made sense because their URLs had been removed, or which were partial sentences from song lyrics which looked like full sentences because they were on lines of their own. Most of the rest just weren’t very interesting.

There were quite a lot of song lyrics which fit into haiku, which suggest tunes to which other haiku can be sung, if the stress patterns match up. I’m not going to put those here though; there are too many, and I could make a separate post about haiku in Jonathan Coulton lyrics, having already compiled a JoCorpus for rhyme.science to find rhymes in. So here are some other categories of haiku I liked. For lack of a better idea, I’ll link the first word of each one to the video it’s from.

Apologies about my camerawork

Also, there’s a lot
of background noise so the sound
isn’t very good.

There was a little
too much light and sound for my
poor little camera. 🙂

But hey, if I’d brought
my external microphone,
it would have got wet.

I’m so sad that I
had to change batteries or
something part-way through. 😦

Who do I look like,
Joe Covenant in Glasgow
in 2008?

Now the guitar is
out of tune and my camera
is out of focus.

Performers being their typical selves

John Roderick:

Eventually
they get around to singing
the song Cinnamon.

Aimee Mann asks John
Roderick to play one of
his songs (which he wrote.)

Jim Boggia:

But first, he gives us
a taste of what he’s really
famous for: tuning.

And now he’s lost his
voice, so it’s going to be
great for everything.

Cody Wymore:

Cody Wymore can’t
do a set without Stephen
Sondheim in it.

Cody horns in on
it anyway by adding
a piano part.

He pauses time for
a bit so nobody knows
he was unprepared.

It’s about being
in a room full of people
and feeling alone.

Paul and Storm:

Why does every new
verse of their song keep taking
them so goddamn long?

Little did I know
that four other people would
throw panties at Paul.

Ted Leo:

We’re gonna bring the
mood down a little bit, but
maybe lift it up!

Nerf Herder:

Meanwhile, they have to
fix up the drums because I
guess they rocked too hard.

Zoe and Brian Gray:

It’s For the Glory
of Gleeble Glorp, which isn’t
a euphemism.

Zoe Gray has to
follow Brian Gray’s songs from
the Gleebleverse.

Clint McElroy:

He’s here to perform
for us an amazing act
of léger de main.

Travis McElroy:

Travis gets up on
stage and holds a small doll’s head
in a creepy way.

which brings us to Jonathan Coulton:

He loves us and is
very glad to be with us.
This is Creepy Doll.

Jonathan Coulton
remarks on the lax rhyming
in God Save The Queen.

Jonathan will use
Jim’s capo, and he will give
it back afterwards.

Jonathan did not
know this was going to be
a cardio set.

That guy Paul has been
seeing every goddamned day
for the last two months.

MC Frontalot:

MC Frontalot
talks about samples and tells
us what hiphop is.

Jean Grae:

It’s not because she’s
a lady, but because she’s
an alcoholic.

She feels like she should
get a guitar case, even
without a guitar.

Jon Spurney:

Jon Spurney rocks out
on the guitar solo, as
he is wont to do.

Me:

Eventually,
at about 6:38,
we get to the point.

The ship’s IT guy:

He has been very
glad to meet us, but he’s not
sad to see us leave.

Red Team Leader:

Red Leader has some
announcements to make before
the final concert.

The Red Team didn’t
mind, because we’re the team that
entertains ourselves.

All the JoCo Cruise performers in the second half of the last show:

Let’s bring Aimee Mann
back out to the stage to join
the Shitty Bar Band.

We now get into
the unrehearsed supergroup
section of the show.

JoCo Cruise hijinks

This is the last show,
unless we’re quarantined on
the ship for a while!

Half of those palettes
were 55-gallon drums
of caveat sauce.

This pun somehow leads
to a sad Happy Birthday
for Paul Sabourin.

Paul Sabourin points
out Kendra’s Glow Cloud dress in
the front row (all hail!)

They talk about why
they did note-for-note covers
instead of new takes.

Make It With You by
Bread, which has even better
string writing than Swift.

So by Friday night,
they’d written this musical
about JoCo Cruise.

A plan to take over the world:

Here’s how it’s going
to work: first we’re going to
have a nice dinner.

And once we have our
very own cruise ship, we shall
dominate the seas.

Some Truth:

An actual cake
which is not a lie. It was
delicious and moist.

It was delicious
and moist. This is Drew’s body
given up for us.

Questions and answers:

What do you do when
you reach the limits of your
own understanding?

When she reaches the
limits of her knowledge, she
says she doesn’t know.

the green people with
buttons who are aliens
wanting to probe you

Wash your hands! Do you
need to take your life jackets
to the safety drill?

What about water,
though? Where do you sign up for
the specialty lunch?

Calls to action

All this and more can
be real if you book yourself
a berth on that boat.

It was supported
by her Patreon patrons.
You could be one too!

If you want to hear
him sing more covers this way,
back this Kickstarter:

That will do for now. Next perhaps I’ll make word clouds of my YouTube descriptions from various time periods, to show what I was uploading at the time. Or perhaps I’ll feed the descriptions into the app I wrote to create the data for rhyme.science, see what the most common rhymes are, and write a poem about them, as I did with Last Chance to See.

Eventually, some of the content I create from my YouTube metadata will make it into a YouTube video of its own — perhaps finally a real channel trailer. But what will I write in the description and title, and will I have to calculate the steady state of a Markov chain to make sure it doesn’t affect the data it shows?

 

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Ajvar Nachos


I don’t know how ajvar is usually used, and I’m not even sure I pronounce it correctly, but many years ago I discovered that it makes a great base for nachos, or just a great nacho topping by itself, so with this recipe, I may offend Balkan and Mexican chefs alike. Quantities are all approximate… use as much of each thing as you feel like.


Ingredients:

A bag of Doritos behind a can of kidney beans, a jar of Ajvar, some crème fraîche, a bag of grated cheese, and an onion.1 jar ajvar (spicy or mild, depending on how spicy you want your nachos and how many other ingredients you’ll be adding)
1 or 2 large packets of corn chips (you probably need more than you think. I prefer nacho cheese flavour.)
1 small container of sour cream or crème fraîche (you probably need less than you think)
plenty of grated cheese (I find Sbrinz cheese is great for nachos.)
1 or 2 onions (optional)
1 can red kidney beans (optional)
some kind of hot sauce, to taste (optional)

Directions:

An orange mixture with some beans visible, covered in grated cheese and a few dollops of sour cream, all on a white plate with some triangular corn chips, on top of a sparkly place mat. The corn chip bag is nearby.Chop and fry the onion(s), if using, in a large frying pan. Empty the ajvar into the pan. Swill a small amount of liquid from the kidney bean can, if using, or water, in the ajvar jar to dislodge any additional ajvar, and pour that into the pan. Drain the rest of the liquid from the kidney beans (if using) and empty them into the pan. Stir and heat up the mixture to a good eating temperature. Add hot sauce to taste, if using.

To serve, put a few large serving spoonfuls of the mixture onto a plate. Cover it with a layer of grated cheese, and if necessary, microwave briefly to melt the cheese. Add a dollop of sour cream. Serve the corn chips on the side so they stay crunchy and are less messy to handle.

To eat, scoop up some ajvar mixture, cheese, and a little of the sour cream with a corn chip, and put it in your mouth. You probably know how to do the rest.

Serves 2 or 3, as a main dish, if all optional ingredients are used.

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