AI for Social Good – Google AI Weblog

Google’s AI for Social Good crew consists of researchers, engineers, volunteers, and others with a shared concentrate on optimistic social affect. Our mission is to display AI’s societal profit by enabling real-world worth, with initiatives spanning work in public health, accessibility, crisis response, climate and energy, and nature and society. We imagine that one of the best ways to drive optimistic change in underserved communities is by partnering with change-makers and the organizations they serve.

On this weblog submit we focus on work performed by Project Euphonia, a crew inside AI for Social Good, that goals to improve automatic speech recognition (ASR) for folks with disordered speech. For folks with typical speech, an ASR mannequin’s phrase error fee (WER) might be lower than 10%. However for folks with disordered speech patterns, reminiscent of stuttering, dysarthria and apraxia, the WER might attain 50% and even 90% relying on the etiology and severity. To assist handle this drawback, we labored with greater than 1,000 contributors to collect over 1,000 hours of disordered speech samples and used the info to point out that ASR personalization is a viable avenue for bridging the efficiency hole for customers with disordered speech. We have proven that personalization might be profitable with as little as 3-4 minutes of training speech utilizing layer freezing techniques.

This work led to the event of Project Relate for anybody with atypical speech who may gain advantage from a customized speech mannequin. Inbuilt partnership with Google’s Speech team, Mission Relate allows individuals who discover it laborious to be understood by different folks and know-how to coach their very own fashions. Individuals can use these customized fashions to speak extra successfully and achieve extra independence. To make ASR extra accessible and usable, we describe how we fine-tuned Google’s Universal Speech Model (USM) to higher perceive disordered speech out of the field, with out personalization, to be used with digital assistant applied sciences, dictation apps, and in conversations.

Addressing the challenges

Working carefully with Mission Relate customers, it grew to become clear that customized fashions might be very helpful, however for a lot of customers, recording dozens or a whole bunch of examples might be difficult. As well as, the customized fashions didn’t all the time carry out effectively in freeform dialog.

To deal with these challenges, Euphonia’s analysis efforts have been specializing in speaker impartial ASR (SI-ASR) to make fashions work higher out of the field for folks with disordered speech in order that no further coaching is important.

Prompted Speech dataset for SI-ASR

Step one in constructing a sturdy SI-ASR mannequin was to create consultant dataset splits. We created the Prompted Speech dataset by splitting the Euphonia corpus into prepare, validation and take a look at parts, whereas making certain that every cut up spanned a variety of speech impairment severity and underlying etiology and that no audio system or phrases appeared in a number of splits. The coaching portion consists of over 950k speech utterances from over 1,000 audio system with disordered speech. The take a look at set accommodates round 5,700 utterances from over 350 audio system. Speech-language pathologists manually reviewed the entire utterances within the take a look at set for transcription accuracy and audio high quality.

Actual Dialog take a look at set

Unprompted or conversational speech differs from prompted speech in a number of methods. In dialog, folks communicate sooner and enunciate much less. They repeat phrases, restore misspoken phrases, and use a extra expansive vocabulary that’s particular and private to themselves and their group. To enhance a mannequin for this use case, we created the Actual Dialog take a look at set to benchmark efficiency.

The Actual Dialog take a look at set was created with the assistance of trusted testers who recorded themselves talking throughout conversations. The audio was reviewed, any personally identifiable info (PII) was eliminated, after which that knowledge was transcribed by speech-language pathologists. The Actual Dialog take a look at set accommodates over 1,500 utterances from 29 audio system.

Adapting USM to disordered speech

We then tuned USM on the coaching cut up of the Euphonia Prompted Speech set to enhance its efficiency on disordered speech. As an alternative of fine-tuning the complete mannequin, our tuning was primarily based on residual adapters, a parameter-efficient tuning strategy that provides tunable bottleneck layers as residuals between the transformer layers. Solely these layers are tuned, whereas the remainder of the mannequin weights are untouched. We now have previously shown that this strategy works very effectively to adapt ASR fashions to disordered speech. Residual adapters had been solely added to the encoder layers, and the bottleneck dimension was set to 64.

Outcomes

To guage the tailored USM, we in contrast it to older ASR fashions utilizing the 2 take a look at units described above. For every take a look at, we examine tailored USM to the pre-USM mannequin finest suited to that job: (1) For brief prompted speech, we examine to Google’s manufacturing ASR mannequin optimized for brief type ASR; (2) for longer Actual Dialog speech, we examine to a mannequin trained for long form ASR. USM enhancements over pre-USM fashions might be defined by USM’s relative measurement enhance, 120M to 2B parameters, and different enhancements mentioned within the USM blog post.

Mannequin phrase error charges (WER) for every take a look at set (decrease is best).

We see that the USM tailored with disordered speech considerably outperforms the opposite fashions. The tailored USM’s WER on Actual Dialog is 37% higher than the pre-USM mannequin, and on the Prompted Speech take a look at set, the tailored USM performs 53% higher.

These findings recommend that the tailored USM is considerably extra usable for an finish consumer with disordered speech. We are able to display this enchancment by taking a look at transcripts of Actual Dialog take a look at set recordings from a trusted tester of Euphonia and Mission Relate (see beneath).

A comparability of the Pre-USM and tailored USM transcripts revealed some key benefits:

• The primary instance exhibits that Tailored USM is best at recognizing disordered speech patterns. The baseline misses key phrases like “XBox” and “controller” which might be essential for a listener to grasp what they’re attempting to say.
• The second instance is an efficient instance of how deletions are a major concern with ASR fashions that aren’t educated with disordered speech. Although the baseline mannequin did transcribe a portion accurately, a big a part of the utterance was not transcribed, dropping the speaker’s meant message.

Conclusion

We imagine that this work is a vital step in direction of making speech recognition extra accessible to folks with disordered speech. We’re persevering with to work on enhancing the efficiency of our fashions. With the fast developments in ASR, we intention to make sure folks with disordered speech profit as effectively.

Acknowledgements

Key contributors to this undertaking embrace Fadi Biadsy, Michael Brenner, Julie Cattiau, Richard Cave, Amy Chung-Yu Chou, Dotan Emanuel, Jordan Inexperienced, Rus Heywood, Pan-Pan Jiang, Anton Kast, Marilyn Ladewig, Bob MacDonald, Philip Nelson, Katie Seaver, Joel Shor, Jimmy Tobin, Katrin Tomanek, and Subhashini Venugopalan. We gratefully acknowledge the help Mission Euphonia acquired from members of the USM analysis crew together with Yu Zhang, Wei Han, Nanxin Chen, and lots of others. Most significantly, we wished to say an enormous thanks to the two,200+ contributors who recorded speech samples and the numerous advocacy groups who helped us join with these contributors.

¹Audio quantity has been adjusted for ease of listening, however the authentic recordsdata could be extra in step with these utilized in coaching and would have pauses, silences, variable quantity, and so forth. ^↩