Is Word Error Rate a Good Metric for Speech Recognition Models? | Hacker News Reader

Is Word Error Rate a Good Metric for Speech Recognition Models?

assemblyai.com

29 pointsdylanbfox30 comments

I ship speech recognition to users for full computer control (mixed commands and dictation) with a very tight feedback loop. I get a lot of direct feedback about any common issues.

One time I beta tested a new speech model I trained that scored very well on WER. Something like 1/2 to 1/3 as many errors as the previous model.

This new model frustrated so many users, because the _nature_ of errors was much worse than before, despite fewer overall errors. The worst characteristic of this new model was word deletions. They occurred far more often. This makes me think we should consider reporting insertion/replacement/deletion as separate % metrics (which I found some older whitepapers did!)

We have CER (Character Error Rate), which is more granular and helps give a sense of whether entire words are wrong (CER = WER) or mostly just single letters (CER much lower than WER).

I'd welcome some ideas for new metrics, even if they only make sense for evaluating my own models against each other.

GPT2 perplexity?

Phoneme aware WER that penalizes errors more if they don't sound "alike" to the ground truth? (Because humans can in some cases read a transcription where every word is wrong, 100% WER, and still figure out by the sound of each incorrect word what the "right" words would have been)

"edge" error rate, that is, the likelihood that errors occur at the beginning / end of an utterance rather than the middle?

Some kind of word histogram, to demonstrate which specific words tend to result in errors / which words tend to be recognized well? One of the tasks I've found hardest is predicting single words in isolation. I'd love a good/standard (demographically distributed) dataset around this, e.g. 100,000 English words spoken in isolation by speakers with good accent/dialect distribution. I built a small version of this myself and I've seen WER >50% on it for many publicly available models.

More focus on accent/dialect aware evaluation datasets?

+ From one of my other comments here: some ways to detect error clustering? I think ideally you want errors to be randomly distributed rather than clustered on adjacent words or focused on specific parts of an utterance (e.g. tend to mess up the last word in the utterance)

At Amazon I set up an evaluation approach based on whether the system completed the desired task (in that context it was "did the search result using the speech recognition return the same set of items to buy as the transcript.)

https://scholar.google.com/citations?view_op=view_citation&h...

Interesting. It seems like in the "real world" WER is not really the metric that matters, it's more about "is this ASR system performing well to solve my use case" - which is better measured through task-specific metrics like the one you outlined your paper.

A pure ASR analog of this is how many/how much continuous utterances it enables. When I use tools like the one lunixbochs builds (including his own) the challenge as a user is trading of doing little bits at a time (slow, but easier to go back and correct) vs saying a whole ‘sentence’ in one go (fast and natural but you’re probably going to have to go back and edit/try again).

Sentence/command error rate (rate of 100% correct sentences/commands that don’t need any editing or re-attempting) is a decent proxy for this. It’s no silver bullet, but it more directly measures how frustrated your users will be.

If you really wanted to take care of the issues in the article, you could interview a bunch of users and find what percent of the, would go back and edit each kind of mistake (if 70% would have to go back and change ‘liked’ to ‘like’ then it’s 70% as bad as substituting ‘pound’ for ‘around’ which presumably every user will go back and edit).

The infuriating thing as a user is when metrics don’t map to the extra work I have to do.

> vs saying a whole ‘sentence’ in one go (fast and natural but you’re probably going to have to go back and edit/try again)

"probably going to have to go back and edit" is generally not the case with my Conformer model, which allows fast paced usage like this with practice: https://twitter.com/lunixbochs/status/1378159234861264896

Unfortunately that was the model I had in mind when I wrote that. I used it for maybe a month (I'm pretty sure), and my experience just wasn't as good as yours. It may be better than what preceded it, but it still drove me crazy. I came away with the conclusion that ASR as a technology just isn't there yet.

(and the conclusion that I need to prevent the return of RSI at all costs from now on. Don't get me wrong, I'm very thankful that talon does as well as it does. It was a job saver.)

Are you referring to the test you mentioned in this thread? https://news.ycombinator.com/item?id=26784732

If so, December predates Conformer, so you're talking about the sconv model, which is the model I was complaining about upthread - it was very polarizing with users, and despite the theoretical WER improvements, the errors were much more catastrophic than the model that preceded it.

In either case, I'm constantly making improvements - I'm in the middle of a retrain that fixes some of the biggest issues (such as misrecognizing some short commands as numbers), and I've done a lot of other work recently that has really polished up the experience with the existing model.

I totally forgot about that conversation! Yeah I must be referring to sconv then. I was thinking of the new custom-trained model you were releasing to your paid beta patreon subscribers, and confused the two.

As a side rant, it turned out that simply stepping away from work for a few weeks around the holidays nearly fixed my RSI, which makes me so sad about the nature of my career whenever it crops back up.

Btw, any chance you've done any work on the `phones` or related tooling? I remember that (and editing in general) being a pain point.

Yeah for sure, breaks are really important.

sconv was especially disappointing because it looked so good on metrics during my training, but the cracks really started to show once it entered user testing. Conformer has been so much less stressful in comparison because most user complaints are about near misses (or completely ambiguous speech where the output is not _wrong_ per se if you listen to the audio) rather than catastrophic failure.

There's another interesting emergent behavior with my user base as I make improvements, which is that as I release improved models allowing users to speak faster without mistakes, some users will speak even faster until there are mistakes again.

Edit: Yep! There have been several improvements on editing, though that's more in the user script domain and my work has still been mostly on the backing tech. I'm planning on working on "first party" user scripts in the future where that stuff is more polished too.

> as I release improved models allowing users to speak faster without mistakes, users will speak even faster until there are mistakes again.

LOL. Users will be users! That's a hilarious case study, thanks for sharing.

> Yep! There have been several improvements on editing, though that's more in the user script domain and my work has still been mostly on the backing tech. I'm planning on working on "first party" user scripts in the future where that stuff is more polished too.

That would be wonderful! If you haven't seen them, I'd suggest looking at Serenade (also ASR) and Nebo (handwriting OCR on ipad) as interesting references for editing UI. They seem to have tight integration between the recognition and editing steps, letting errors be painless to fix by exposing alternative recognitions at the click of a button or short command. It lets them make x% precision@n as convenient as x% accuracy.

I would say not quite as convenient, because they lean on that UI to also make you constantly confirm top-1 commands that would've worked fine. As you can see in my Conformer demo video I can hit top-1 so reliably I don't even need to wait to look at the command before I start saying the next one.

Your story reminds me of what's happened to Google's voice recognition over the last five years or so. It used to mis-hear words, but now it actively alters grammar and inserts words that sound nothing like what I actually said. Just try getting it to type the word "o'clock".

At face value, actively altering grammar to words that you didn't say sounds like the language model is very heavily weighed. I'm curious if you mean the keyboard? Because that recently switched to on device I think, which means much smaller models and compute used.

The behavior I'm complaining about happens on both, though as best I can tell the voice typing decides whether to use on-device or cloud-based depending on the conditions when you use it. If you cut your data off you'll get word-by-word recognition, whereas most of the times you're connected the whole sentence will pop in at the same time indicating it used the cloud.

Yes! Perplexity is a great idea. Although you could technically have a low perplexity prediction that is not similar to the ground truth transcription.

CER is definitely more granular. There are papers that basically count Deletions, for example, as 0.5(D) when calculating WER - since they consider Deletions "less bad", but if these weights aren't standardized then WER scores will be super hard to compare.

Personally I think some metric including some type of perplexity is the way to go.

So I was looking at SotA loss functions from a few years ago that weighted the CTC loss by the WER of the decoded phrase.

Could we generalize the WER weighting to optimize for the domain?

Something like

weight = w1 * WER + w2 * phonetic similarity + ...

which also requires a hyperparameter search... But we are already dumping so many GPU hours here.

I assume this is already being investigated by Google, though?

There are some a similar techniques where you use evaluation metrics to decide which data to train on each epoch.

I wonder if you could make that parameter trainable instead of using a hyperparameter search for it.

For phonetic similarity I've been playing with a dual objective system that could be promising.

I work in this domain, dealing exclusively with recognition for assistants, which is different from dictation. We measure three things, top down:

- whole phrase intent recognition rates. Run the transcribed phrase through a classifier to identify what the phrase is asking for, and compare that to what was expected, calculating an F1 score. Keep track of phrases that score poorly: they need to be improved.

- "domain term" error rate. Identify a list of key words that important to the domain and that must be recognized well, such as location names, products to buy, drug names, terms of art. For every transcribed utterance, measure the F1 score for those terms, and track alternatives created in confusion matrix. This results in distilled list of words the system gets wrong and what is heard instead.

- overall word error rate, to provide a general view of model performance.

Feels like a lot of the counter examples listed involve contractions and conjugation errors. Saying 'like' and 'liked' are different words is a strong interpretation. Similarly, 'I am' and 'I'm' are really not distinct words so counting that toward an error rate is a bit too literal. The objections could be solved by a decent parser. That said, weighting insertions and deletions equally is clearly a problem. Certain words ought to have more weight in a model. Weighting words by something like 1/log(frequency) might be a good start since less common words tend to be more important for meaning.

Interesting, maybe instead of my proposed perplexity metric, we measure the difference in both utterance and per-word perplexity between ground truth and output with a strong language model? Ideally it's low - the language model should consider each predicted word to be "about as likely in context" as the closest ground truth words.

In other words, measure LM perplexity on the ground truth words, then on the predicted words, and minimize the difference in perplexities. Ideally with a general model like GPT2 or BERT or something that you aren't using anywhere in your actual ASR.

This may even be more tolerant of errors in the ground truth transcription than raw WER

> since less common words tend to be more important for meaning.

Exactly. Errors with proper nouns are usually more problematic than errors with stop words, yet they're weighted equally in the WER calculation. Ie, deleting "Bob" and "but" both count as a deletion of the same degree according to WER, but we as humans know that deleting "Bob" is potentially a lot more problematic than deleting "but".

You could weigh insertions by how much perplexity they add (sum), deletions by how much perplexity they remove (-sum), and replacements by how big the ppl difference is in the replaced word (abs(sum)). And report this as a 4-part score (combined mean, then separate i/d/r). Lower is better.

Theory being you don't want to add or remove confusing words, but common stop words are less of an issue.

I'm not sure how this interacts with a multi word replacement, where the new words together make sense but independently make no sense to the LM.

I'm working on a similar domain, music transcription. The challenge is to estimate note values (how many beats is a note supposed to be as represented in the score?) and I'm not sure what would be the a good way to measure transcription accuracy. The naive note error rate cannot capture whether my model successfully detects certain musical structure, syncopation, dotted rhythms etc

Related, are there better representations for music than standard notation (or MIDI)?

I'm wondering what the higher convolution levels could look like, if this was a CNN analyzing an image. Something between a the complete Ableton/Logic export and a MIDI file. Being able to capture the "feel" of a song (or a section within a song) strikes me as an important milestone towards designing really good generative music.

Maybe some kind of alignment metric, to measure how far off on timing notes tend to be?

I can also imagine a generalized "local error rate" which measures how far away errors tend to be from each other. If errors tend to be clustered, I would guess that's showing inability to follow some musical pattern. I think you'd want errors to appear randomly distributed rather than clustered. (This metric might make sense for speech too)

Hey, you should drop me an email (info in my HN bio). This is a passion of mine and I'm always up for chatting about it.

Sure thing!

You might want to consider comparing generated sound files, rather than abstract notion. If you have the ground truth notion, render that using the same mechanism as your transcription. Then you can use various spectral comparison techniques on the sound, including things like fourier analysis to compare structure.

If the target of ASR would be document retrieval, it would make sense to apply the same (easy) transformations before calculating WER. Think: function word removal, unsplitting remaining contractions, and stemming. That would take out some of the problems, while staying true to the target. Aren't there any old-school linguists working on this?

Rare example where Betteridge's law of headlines is wrong.

One clever metric that Google mentioned in their early ASR papers was interesting: "WebScore". Basically, they consider a hypothesis transcription to have errors only if it produces a different top web search result. [1] WebScore and WER always seemed to track each other though.

[1] https://static.googleusercontent.com/media/research.google.c...

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