Low-Resource Contextual Topic Identification on Speech

Chunxi Liu, Matthew Wiesner, Shinji Watanabe, Craig Harman, Jan Trmal, Najim Dehak, Sanjeev Khudanpur

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

In topic identification (topic ID) on real-world unstructured audio, an audio instance of variable topic shifts is first broken into sequential segments, and each segment is independently classified. We first present a general purpose method for topic ID on spoken segments in low-resource languages, using a cascade of universal acoustic modeling, translation lexicons to English, and English-language topic classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large improvements. In particular, we propose an attention-based contextual model which is able to leverage the contexts in a selective manner. We test both our contextual and non-contextual models on four LORELEI languages, and on all but one our attention-based contextual model significantly outperforms the context-independent models.

本文言語English
ホスト出版物のタイトル2018 IEEE Spoken Language Technology Workshop, SLT 2018 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ656-663
ページ数8
ISBN(電子版)9781538643341
DOI
出版ステータスPublished - 2019 2 11
外部発表はい
イベント2018 IEEE Spoken Language Technology Workshop, SLT 2018 - Athens, Greece
継続期間: 2018 12 182018 12 21

出版物シリーズ

名前2018 IEEE Spoken Language Technology Workshop, SLT 2018 - Proceedings

Conference

Conference2018 IEEE Spoken Language Technology Workshop, SLT 2018
国/地域Greece
CityAthens
Period18/12/1818/12/21

ASJC Scopus subject areas

  • コンピュータ ビジョンおよびパターン認識
  • 人間とコンピュータの相互作用
  • 言語学および言語

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