Challenges in Automatic Speech Recognition 2010-2020: Speech Technology for the Next Decade - Visions from Academia and Industry Ciprian Chelba, Michiel Bacchiani, Johan Schalkwyk {ciprianchelba,michiel,johans}@google.com

Google

09/29/2010 Ciprian Chelba et al., Challenges in ASR – p. 1

Case Study:Google Search by Voice

Carries 25% of USA Google mobile search queries! What contributed to success: clearly set user expectation by existing text app excellent language model built from query stream clean speech: users are motivated to articulate clearly phones do high quality speech capture speech tranferred error free to server over IP Challenges: Making and measuring progress: manually transcribing data is at about same word error rate as system (15%) 09/29/2010 Ciprian Chelba et al., Challenges in ASR – p. 2

Case Study: Google Labs GAudi Demo This was the study for the YouTube feature that is now launched for all and integrated with translation. Main challenge: lack of coverage due to ASR limitations: noise-robustness speaker/accent/channel variability language model mismatches web is multi-lingual

09/29/2010 Ciprian Chelba et al., Challenges in ASR – p. 3

ASR for Retrieval and Ranking

On large document collections search is truly about Precision@N. There is seldom a good reason to replace a result in the top-N with one that has hits in the (noisy) ASR transcript. Future directions: improve retrieval for "hard queries" which return very few documents based strictly on keyword hits in the text metadata speech-rich sub-domains such as lectures/talks in English recorded in a controlled setup where current ASR capabilities are adequate after manual tuning to the sub-domain. 09/29/2010 Ciprian Chelba et al., Challenges in ASR – p. 4

Core Technology

Current state: automatic speech recognition is incredibly complex problem is fundamentally unsolved data availability and computing have changed significantly since the mid-nineties Challenges and Directions: re-visit (simplify!) modeling choices made on corpora of modest size; 2-3 orders of magnitude more data is available multi-linguality built-in from start noise-robustness and speaker/channel variability

09/29/2010 Ciprian Chelba et al., Challenges in ASR – p. 5

Challenges in Automatic Speech Recognition - Research at Google

Case Study:Google Search by Voice. Carries 25% of USA Google mobile search queries! ... speech-rich sub-domains such as lectures/talks in ... of modest size; 2-3 orders of magnitude more data is available multi-linguality built-in from start.

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