DocumentCode :
730806
Title :
Automatic pronunciation verification for speech recognition
Author :
Rao, Kanishka ; Fuchun Peng ; Beaufays, Francoise
Author_Institution :
Google Inc., Mountain View, CA, USA
fYear :
2015
fDate :
19-24 April 2015
Firstpage :
5162
Lastpage :
5166
Abstract :
Pronunciations for words are a critical component in an automated speech recognition system (ASR) as mis-recognitions may be caused by missing or inaccurate pronunciations. The need for high quality pronunciations has recently motivated data-driven techniques to generate them [1]. We propose a data-driven and language-independent framework for verification of such pronunciations to further improve the lexicon quality in ASR. New candidate pronunciations are verified by re-recognizing historical audio logs and examining the associated recognition costs. We build an additional pronunciation quality feature from word and pronunciation frequencies in logs. A machine learned classifier trained on these features achieves nearly 90% accuracy in labeling good vs bad pronunciations across all languages we tested. New pronunciations verified as good may be added to a dictionary, while bad pronunciations may be discarded or sent to experts for further evaluation. We simultaneously verify 5,000 to 30,000 new pronunciations within a few hours and show improvements in the ASR performance as a result of including pronunciations verified by this system.
Keywords :
feature extraction; speech recognition; ASR; automated speech recognition system; automatic pronunciation verification; data-driven techniques; lexicon quality; machine learned classifier; misrecognitions; pronunciation quality feature; recognizing historical audio logs; Dictionaries; Measurement; Speech recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
Conference_Location :
South Brisbane, QLD
Type :
conf
DOI :
10.1109/ICASSP.2015.7178955
Filename :
7178955
Link To Document :
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