liao15@interspeech_2015@ISCA

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#1 Large vocabulary automatic speech recognition for children [PDF] [Copy] [Kimi1] [REL]

Authors: Hank Liao, Golan Pundak, Olivier Siohan, Melissa K. Carroll, Noah Coccaro, Qi-Ming Jiang, Tara N. Sainath, Andrew Senior, Françoise Beaufays, Michiel Bacchiani

Recently, Google launched YouTube Kids, a mobile application for children, that uses a speech recognizer built specifically for recognizing children's speech. In this paper we present techniques we explored to build such a system. We describe the use of a neural network classifier to identify matched acoustic training data, filtering data for language modeling to reduce the chance of producing offensive results. We also compare long short-term memory (LSTM) recurrent networks to convolutional, LSTM, deep neural networks (CLDNN). We found that a CLDNN acoustic model outperforms an LSTM across a variety of different conditions, but does not specifically model child speech relatively better than adult. Overall, these findings allow us to build a successful, state-of-the-art large vocabulary speech recognizer for both children and adults.

Subject: INTERSPEECH.2015 - Speech Processing