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Improving Children's Speech Recognition through Out-of-Domain Data Augmentation

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publicationInterspeech 2016
Pages1598-1602
Number of pages5
DOIs
Publication statusPublished - 12 Sep 2016
EventInterspeech 2016 - San Francisco, United States
Duration: 8 Sep 201612 Sep 2016
http://www.interspeech2016.org/

Conference

ConferenceInterspeech 2016
CountryUnited States
CitySan Francisco
Period8/09/1612/09/16
Internet address

Abstract

Children's speech poses challenges to speech recognition due to strong age-dependent anatomical variations and a lack of large, publicly-available corpora. In this paper we explore data augmentation for children's speech recognition using stochastic feature mapping (SFM) to transform out-of-domain adult data for both GMM-based and DNN-based acoustic models. We performed experiments on the English PF-STAR corpus, augmenting using WSJCAM0 and ABI. Our experimental results indicate that a DNN acoustic model for childrens speech can make use of adult data, and that out-of-domain SFM is more accurate than in-domain SFM.

Event

Interspeech 2016

8/09/1612/09/16

San Francisco, United States

Event: Conference

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