An experimental study on structural-MAP approaches to implementing very large vocabulary speech recognition systems for real-world tasks

Abstract

In this paper we present an experimental study exploiting structural Bayesian adaptation for handling potential mismatches between training and test conditions for real-world applications to be realized in our multilingual very large vocabulary speech recognition (VLVSR) system project sponsored by MOTIE (The Ministry of Trade, Industry and Energy), Republic of Korea. The goal of the project is to construct a national-wide VLVSR cloud service platform for mobile applications. Besides system architecture design issues, at such a large scale, performance robustness problems, caused by mismatches in speakers, tasks, environments, and domains, etc., need to be taken into account very carefully as well. We decide to adopt adaptation, especially the structural MAP, techniques to reduce system accuracy degradation caused by these mismatches. Being part of an ongoing project, we describe how structural MAP approaches can be used for adaptation of both acoustic and language models for our VLVSR systems, and provide convincing experimental results to demonstrate how adaptation can be utilized to bridge the performance gap between the current state-of-the-art and deployable VLVSR systems.