Last year my friend Phil from SkyStudio Wien called me up out of the blue to ask if I wanted to record some poetry, so I did! I went in for another session later in the year, and that time I was more prepared — I gathered up everything I could find that I thought was good and made some kind of sense without too much explanation. We ended up with 39 poems recorded, so in order to bring the total up to 42, I added Why I Perform At Open Mics (previously released on Bandcamp as a single) and a few songs recorded in studios with Joey Marianer. It comes in at just under an hour — some of the tracks are very short #NanoRhymo poems. It’s called Wake Up Gasping.
A lot of these poems have been previously published on this blog in some form, but some haven’t. I included some poems I wrote before I started this blog (the oldest, Shooting Star, being from around 1996) and some I’d written more recently but which I’d only performed with sound effects (Negative Return, sometimes followed by Down while the noise was still trailing off) or just always thought would work better spoken than read (A Couple of Problems.)
The title comes from a line in A Skirmish [With My Least-Favourite Body Part] which I always thought would be a great name for a hard-hitting collection of powerful, emotional poetry. I do not think that’s what this is, but at least with 42 tracks, it looks like I was holding my breath for a while and finally let everything out.
The cover art is by Joseph Camann of The Camannwordsmith Patreon. I started out without much idea of what I wanted on the cover, which was great because his art is mostly abstract, but after looking through some of his existing art for ideas I thought of having the lost astronaut from Down floating through a colourful space-y background. Joseph has a lot going on: music, stories, paintings, poems (sometimes read to puppies), reaction videos, even wearable art, in case you’d like something like this album cover but on a dress.
Some of my tracks have unusual characters in the titles, and I’m happy to report that Bandcamp did not have problems with any of them.
In other news, about 48 hours from now, I’m doing a 50-minute poetry show over Zoom as part of the ‘Shadow Cruise’ of the virtual JoCo Cruise 2021. It will include some poems from the album, but also (thanks to screensharing) some which require or are enhanced by visual aids or additional explanations. I will also be singing a few songs, and reading one poem especially written for the event. Feel free to join — there’s no signup, ticket, or even pants required! As with most JoCo Cruise events I’ve been to over the years, I will post a recording of it on my YouTube channel later if you can’t make it.
Check out the rest of the cruise schedule, and the cruise Discord, too… this year’s cruise is obviously quite different from the usual one in many ways, but still hopefully similar in enough ways that you’ll get a feel for how much it influences my life. One important way it’s different is that it’s completely free and you don’t even have to get out of bed for it, let alone go to an airport and cruise port.
You should also see me participating for the eleventh year in a row in the Fancy Pants Parade. For a while, I was the only person to have submitted a video, but I encouraged some friends to (including some clients of Chromatic Verse Wearable Art, by the same person who designed my album cover) so that I wouldn’t just win by default. Now I can win by crushing the hopes and dreams of my friends! Later, I will post a short making-of video about the pants I appear in.
This weekend my friend Rob Lambert asked me to lend my vocal stylings to an animation he was making about his experience of suddenly getting sick. While my vocals are not nearly as stylish as his, I did indeed record my part — some scripted by Rob, some my own reactions to what he had to say. I think it turned out great; check it out!
I think this video is a good reminder of the second point in my Accessibility is for Everyone article. Learn to notice, appreciate, and contribute to the rabbit hole climbing harnesses around you before you need them.
If you want to see more from Rob, check out his LinkedIn or subscribe to his YouTube channel, which also has some cool CERN stuff. On the subject of LinkedIn, here’s mine! I’m currently looking for full-time or freelance work, and I’d love to work on something related to accessibility, or any of the other things I’ve enthused about or experimented with on this blog.
This year’s MathsJam Annual Gathering was virtual, and rather than singing maths-related parody songs together at the MathsJam Jam, we were encouraged to send videos of ourselves singing them. Sam Hartburn, who has written many lyrics for MathsJam Jam songs, offered to write custom song lyrics for one of the participants. I don’t recall whether this was for the best submission or one chosen at random, but in any case, Joey Marianer got the prize for a cover of Jonathan Coulton’s Mandelbrot Set.
Joey considered giving the prize away, since I already sometimes write lyrics that Joey sings, but instead decided to commission a variation on They Might Not Be Giants, a poem I wrote about science education which Joey set to music. The new version is about someone who has many offers of help to solve a puzzle, but finds satisfaction in solving it on their own.
A while later, TikTok was awash with collaborations on the Wellerman sea shanty, and Daniel Litt wrote a parody of it about elliptic curves. 〈 Berger | Dillon 〉 did a duet with it, and Joey then sang harmony. My only contribution to this one was editing Joey’s video into the others’, since we don’t have TikTok to do it for us. Neither of us knows much about what it’s about, but I bet many of the Wellerman singers don’t know much about the Weller brothers or tonguing either.
We won’t be putting to sea this year, of course, but the 2021 JoCo Cruise is free and virtual, and I am finally going to run a poetry show as a shadow event. Come join us!
I’m excited to discover a more recent viral TikTok is singing a sped-up version of Mike Phirman’s Chicken Monkey Duck! I’m not cool enough to be on TikTok, so I made sure to learn that song before it was cool, just so I wouldn’t surpass my coolness quota. Here’s a playlist of me singing Chicken Monkey Duck, some diagrams I made of the song, and an rtf of it using colours from my personal grapheme–colour synaesthesia, created using an app I wrote a while ago. And for completeness, here’s the Spanish version of it, Pollo Mono Pato, which I think is a bit harder to learn due to all the words having the same number of syllables.
On the subject of Mike Phirman, he’s just released a new album called Activity Books, and it’s great. I’m consistently impressed by how many of his songs do things that songs have rarely or never done before. For instance, Color by Number can probably detect grapheme–colour synaesthesia in young children, by causing them to throw tantrums about the incorrect colour associations. ‘Word Search / Vacuum’ makes me wish there were an alphabet song of the entire International Phonetic Alphabet.
In unrelated news, a while ago I requested a cover of K’s Choice’s song ‘America’ on the CamannWordsmith patreon. That Patreon post has now been made public. The track is also available on the brand new CamannWordsmith Bandcamp page, along with a whole lot of other covers that you can download for free. To bring this back around to relatedness again… CamannWordsmith and I are collaborating on something; watch this space to find out what!
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.
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:
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, and large cursor or font sizes. On macOS when using a projector, you can also use Hover Text, however this does not show up when screensharing over a videoconference. 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.
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.
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!)
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.
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!
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.
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 »
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:
- ‘great’ appears 4402 times
- ‘big’ appears 1351 times
- ‘good’ appears 1105 times
- ‘new’ appears 1034 times
- ‘many’ appears 980 times
- ‘last’ appears 809 times
- ‘best’ appears 724 times
- ‘other’ appears 719 times
- ‘fake’ appears 686 times
- ‘American’ appears 592 times
- ‘real’ appears 510 times
- ‘total’ appears 509 times
- ‘bad’ appears 466 times
- ‘first’ appears 438 times
- ‘next’ appears 407 times
- ‘wonderful’ appears 375 times
- ‘amazing’ appears 354 times
- ‘only’ appears 325 times
- ‘political’ appears 310 times
- ‘beautiful’ appears 298 times
- ‘fantastic’ appears 279 times
- ‘tremendous’ appears 270 times
- ‘massive’ appears 268 times
- ‘illegal’ appears 254 times
- ‘incredible’ appears 254 times
- ‘nice’ appears 251 times
- ‘strong’ appears 250 times
- ‘greatest’ appears 248 times
- ‘true’ appears 247 times
- ‘major’ appears 243 times
- ‘same’ appears 236 times
- ‘terrible’ appears 231 times
- ‘presidential’ appears 221 times
- ‘much’ appears 217 times
- ‘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’.
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.)
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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!