Large Vocabulary Continuous Speech Recognition System on Japanese Newspaper Reading Task

日语报纸阅读任务的大词汇量连续语音识别系统

基本信息

批准号：
10680368
负责人：
KOHDA Masaki
金额：
$ 2.11万
依托单位：
Yamagata University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-10680368/
关键词：
Large Vocabulary Continuous Speech Recognition Acoustic Model Language Model Decoder Hidden Markov Net N-gram Speaker Adaptation Task Adaptation クラスN-gram パープレキシティ単語誤り率エルゴディックHMM マルチパスサーチ音素グラフ単語グラフ HM-Net SCFG MLLR話者適

项目摘要

We investigated large vocabulary continuous speech recognition (LVCSR) system on Japanese newspaper reading task, and obtained the following results.(1) Acoustic models : A Hidden Markov Network (HM-Net) is a highly accurate and robust acoustic model which represents a tied-state structure of context dependent Hidden Markov Models as a network. We propose a state clustering-based rapid topology design method to generate high accuracy HM-Nets for LVCSR.Furthermore, MLLR (Maximum Likelihood Linear Regression)-based speaker adaptation of acoustic models is investigated, and a regression class selection algorithm based on the BIC principle is proposed.(2) Language models : N-gram task adaptation method is investigated, which uses large corpus of the general task (TI text) and small corpus of the specific task (AD text), and employs a simple weighting to mix TI and AD texts. Furthermore we propose a new SCFG (Stochastic Context Free Grammar) model which uses a phrase-based dependency gramma … More r instead of general CFG.Word error rate in the case of using the mixture model besed on the proposed SCFG model and trigram becomes less than that in the case of using only the trigram.(3) Decoder : We investigate about fast search strategies for LVCSR, and propose a new method - a phoneme-graph-based hypothesis restriction, which effectually prunes the search space. In the proposed method, a phoneme graph is generated at the pre-processing stage, and then the best word sequence is searched while restricting expansion of hypotheses using the information of the phoneme graph at the main recognition stage. In the multiple pass LVCSR system that uses word graph as an intermediate data structure, decoder parameters should be optimized in order to generate a good word graph. A new method to optimize these parameters is proposed. This method uses rescoring of the word graph using bigram LM instead of generating many word graphs for each parameter setting.(4) Software Tool : We describe a statistical language model toolkit for word and class-based n-gram. This toolkit has command-level compatibility with CMU-Cambridge SLM Toolkit, and supports class n-gram and n-gram count mixture as well as combined language model using linear interpolation. Less

我们调查了日本报纸阅读任务上的大型词汇连续语音识别（LVCSR）系统，而Follts（1）声学模型：隐藏的马尔可夫网络（HM -NET）是一种高度准确且强大的声学模型依赖性的马尔可夫模型是LVCSR的GH精度。一般任务的大型语料库（TI文本）和特定任务的小语料库（AD文本），Andle加权以混合ti和AD文本。 trigam：我们调查了lvcsr的快速搜索，并提出了一种新方法 - 基于音素的假设限制，有效地将搜索空间重新处理。在主识别阶段的某些音素图中限制假设的扩展Word Graph使用为每个参数设置生成许多单词图的撤回。 n-gram级计数混音以及使用线性插值的组合语言模型。