16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Distributed Representation-Based Spoken Word Sense Induction

Justin Chiu, Yajie Miao, Alan W. Black, Alexander I. Rudnicky

Carnegie Mellon University, USA

Spoken Term Detection (STD) or Keyword Search (KWS) techniques can locate keyword instances but do not differentiate between meanings. Spoken Word Sense Induction (SWSI) differentiates target instances by clustering according to context, providing a more useful result. In this paper we present a fully unsupervised SWSI approach based on distributed representations of spoken utterances. We compare this approach to several others, including the state-of-the-art Hierarchical Dirichlet Process (HDP). To determine how ASR performance affects SWSI, we used three different levels of Word Error Rate (WER), 40%, 20% and 0%; 40% WER is representative of online video, 0% of text. We show that the distributed representation approach outperforms all other approaches, regardless of the WER. Although LDA-based approaches do well on clean data, they degrade significantly with WER. Paradoxically, lower WER does not guarantee better SWSI performance, due to the influence of common locutions.

Full Paper

Bibliographic reference.  Chiu, Justin / Miao, Yajie / Black, Alan W. / Rudnicky, Alexander I. (2015): "Distributed representation-based spoken word sense induction", In INTERSPEECH-2015, 1358-1362.