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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

(1)声学模型：隐马尔可夫网络(HM-Net)是一种高精度、高健壮性的声学模型，它将上下文相关隐马尔可夫模型的约束结构表示为一个网络。提出了一种基于状态聚类的快速拓扑设计方法来生成高精度的声学模型HM-Net。此外，研究了基于最大似然线性回归(MLLR)的说话人自适应声学模型，并提出了一种基于BIC原则的回归类选择算法。(2)语言模型：研究了N-gram任务自适应方法，该方法使用大语料库(TI文本)和小语料库(AD文本)，并采用简单的加权来混合TI和AD文本。在此基础上，提出了基于短语依存文法…的随机上下文无关文法模型在使用基于SCFG模型的混合模型的情况下，单词错误率比仅使用三元组的情况下要小。(3)解码器：研究了LVCSR的快速搜索策略，并提出了一种新的方法--基于音素图的假设约束，有效地修剪了搜索空间。该方法在预处理阶段生成音素图，然后在主识别阶段利用音素图的信息在限制假设扩展的同时搜索最佳单词序列。在使用字图作为中间数据结构的多程LVCSR系统中，为了生成良好的字图，需要对译码参数进行优化。提出了一种优化这些参数的新方法。该方法使用二元语法LM对单词图进行重新评分，而不是为每个参数设置生成许多单词图。(4)软件工具：我们描述了一个基于单词和类的n元语法统计语言模型工具包。该工具包与CMU-Cambridge SLM工具包具有命令级兼容性，并支持类n元语法和n元语法计数混合以及使用线性内插的组合语言模型。较少