Data augmentation using prosody and false starts to recognize non-native children's speech

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Abstract

This paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Automatic Speech Recognition (ASR) for non-native children's speech. The task is to recognize non-native speech from children of various age groups given a limited amount of speech. Moreover, the speech being spontaneous has false starts transcribed as partial words, which in the test transcriptions leads to unseen partial words. To cope with these two challenges, we investigate a data augmentation-based approach. Firstly, we apply the prosody-based data augmentation to supplement the audio data. Secondly, we simulate false starts by introducing partial-word noise in the language modeling corpora creating new words. Acoustic models trained on prosody-based augmented data outperform the models using the baseline recipe or the SpecAugment-based augmentation. The partial-word noise also helps to improve the baseline language model. Our ASR system, a combination of these schemes, is placed third in the evaluation period and achieves the word error rate of 18.71%. Post-evaluation period, we observe that increasing the amounts of prosody-based augmented data leads to better performance. Furthermore, removing low-confidence-score words from hypotheses can lead to further gains. These two improvements lower the ASR error rate to 17.99%.
Original languageEnglish
Title of host publicationProceedings of Interspeech
PublisherInternational Speech Communication Association
Pages260-264
Number of pages5
Volume2020-October
DOIs
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInterspeech - Shanghai, China
Duration: 25 Oct 202029 Oct 2020
Conference number: 21
http://www.interspeech2020.org/

Publication series

NameInterspeech
ISSN (Electronic)1990-9772

Conference

ConferenceInterspeech
Abbreviated titleINTERSPEECH
CountryChina
CityShanghai
Period25/10/202029/10/2020
Internet address

Keywords

  • Children speech recognition
  • prosody modification
  • SpecAugment
  • hesitation noise
  • confidence score filter

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