Improved subword modeling for WFST-based speech recognition

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

Details

Original languageEnglish
Title of host publicationProceedings of Interspeech 2017
PublisherInternational Speech Communication Association
Pages2551-2555
Number of pages5
StatePublished - Aug 2017
MoE publication typeA4 Article in a conference publication
EventINTERSPEECH -
Duration: 1 Jan 1900 → …

Publication series

NameInterspeech: Annual Conference of the International Speech Communication Association
ISSN (Electronic)1990-9772

Conference

ConferenceINTERSPEECH
Period01/01/1900 → …

Researchers

Research units

Abstract

Because in agglutinative languages the number of observed word forms is very high, subword units are often utilized in speech recognition. However, the proper use of subword units requires careful consideration of details such as silence modeling, position-dependent phones, and combination of the units. In this paper, we implement subword modeling in the Kaldi toolkit by creating modified lexicon by finite-state transducers to represent the subword units correctly. We experiment with multiple types of word boundary markers and achieve the best results by adding a marker to the left or right side of a subword unit whenever it is not preceded or followed by a word boundary, respectively. We also compare three different toolkits that provide data-driven subword segmentations. In our experiments on a variety of Finnish and Estonian datasets, the best subword models do outperform word-based models and naive subword implementations.
The largest relative reduction in WER is a 23% over word-based models for a Finnish read speech dataset. The results are also better than any previously published ones for the same datasets, and the improvement on all datasets is more than 5%.

    Research areas

  • speech recognition, Kaldi, subword modeling, Finnish, Estonian

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