For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
N-gram Approximation of Latent Words Language Models for Domain Robust Automatic Speech Recognition
Ryo MASUMURA Taichi ASAMI Takanobu OBA Hirokazu MASATAKI Sumitaka SAKAUCHI Satoshi TAKAHASHI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2016/10/01
Online ISSN: 1745-1361
Type of Manuscript: Special Section PAPER (Special Section on Recent Advances in Machine Learning for Spoken Language Processing)
Category: Language modeling
language models, domain robustness, latent words language models, n-gram approximation, automatic speech recognition,
Full Text: FreePDF
This paper aims to improve the domain robustness of language modeling for automatic speech recognition (ASR). To this end, we focus on applying the latent words language model (LWLM) to ASR. LWLMs are generative models whose structure is based on Bayesian soft class-based modeling with vast latent variable space. Their flexible attributes help us to efficiently realize the effects of smoothing and dimensionality reduction and so address the data sparseness problem; LWLMs constructed from limited domain data are expected to robustly cover unknown multiple domains in ASR. However, the attribute flexibility seriously increases computation complexity. If we rigorously compute the generative probability for an observed word sequence, we must consider the huge quantities of all possible latent word assignments. Since this is computationally impractical, some approximation is inevitable for ASR implementation. To solve the problem and apply this approach to ASR, this paper presents an n-gram approximation of LWLM. The n-gram approximation is a method that approximates LWLM as a simple back-off n-gram structure, and offers LWLM-based robust one-pass ASR decoding. Our experiments verify the effectiveness of our approach by evaluating perplexity and ASR performance in not only in-domain data sets but also out-of-domain data sets.