Domain Adaptation Based on Mixture of Latent Words Language Models for Automatic Speech Recognition

Ryo MASUMURA  Taichi ASAMI  Takanobu OBA  Hirokazu MASATAKI  Sumitaka SAKAUCHI  Akinori ITO  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.6   pp.1581-1590
Publication Date: 2018/06/01
Publicized: 2018/02/26
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7210
Type of Manuscript: PAPER
Category: Speech and Hearing
domain adaptation,  mixture modeling,  latent words language models,  latent variable space,  automatic speech recognition,  

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This paper proposes a novel domain adaptation method that can utilize out-of-domain text resources and partially domain matched text resources in language modeling. A major problem in domain adaptation is that it is hard to obtain adequate adaptation effects from out-of-domain text resources. To tackle the problem, our idea is to carry out model merger in a latent variable space created from latent words language models (LWLMs). The latent variables in the LWLMs are represented as specific words selected from the observed word space, so LWLMs can share a common latent variable space. It enables us to perform flexible mixture modeling with consideration of the latent variable space. This paper presents two types of mixture modeling, i.e., LWLM mixture models and LWLM cross-mixture models. The LWLM mixture models can perform a latent word space mixture modeling to mitigate domain mismatch problem. Furthermore, in the LWLM cross-mixture models, LMs which individually constructed from partially matched text resources are split into two element models, each of which can be subjected to mixture modeling. For the approaches, this paper also describes methods to optimize mixture weights using a validation data set. Experiments show that the mixture in latent word space can achieve performance improvements for both target domain and out-of-domain compared with that in observed word space.