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Speech Recognition Based on Intelligent Beam Search Algorithm

基于智能波束搜索算法的语音识别

基本信息

批准号：
01460254
负责人：
KOHDA Masaki
金额：
$ 4.42万
依托单位：
Yamagata University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1989
资助国家：
日本
起止时间：
1989 至 1991
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-01460254/
关键词：
Speech Recognition Graph Search A^* Algorithm Dynamic Time Warping Beam Search Vector Quantization Hidden Markov Model Best-First Search DPマッチング予備選択 DPビ-ムサ-チ閾値関数枝刈フレ-ム同期DPマッチング

项目摘要

In a large-vocabulary continuous speech recognition, an investigation of efficient recognition algorithms is extremely important because of executing an enormous computation needed in a matching process within a realistic CPU time. Conventional recognition algorithms based on a dynamic time warping (DTW), a hidden Markov model (HMM) and so on are constructed on the base of an exhaustive search of possible combinations. A dynamic programming technique is introduced to execute the exhaustive search efficiently.The matching process in DTW-based and HMM-based speech recognition systems is regarded as a problem of searching an optimal path through a constrained node. In an application of graph searching algorithms to speechrecognition, two kinds of searching algorithms are effective, that is, a beam searching algorithm and a best-first searching algorithm. A conventional pruning strategy in speech recognition using the beam searching algorithm is based on only a score from the beginning node to the current node. A score estimate from the current node to the terminal node is not used. An A^* algorithm is introduced to speech recognition using the best-first searching algorithm.This report describes new approaches to DTW-based and HMM-based speech recognition algorithms by modeling the matching process from a view point of a graph search. In Chapter I, a DTW-based speech recognition utilizing the beam searching algorithm is described. In Chapter II, a DTW-based speech recognition utilizing the best-first searching algorithm is described. Finally in Chapter III, an HMM-based speech recognition utilizing the best-first searching algorithm is described.

在大词汇量连续语音识别中，高效识别算法的研究极其重要，因为在实际的 CPU 时间内执行匹配过程所需的大量计算。基于动态时间规整（DTW）、隐马尔可夫模型（HMM）等的传统识别算法是在对可能组合的穷举搜索的基础上构建的。引入动态规划技术来有效地执行穷举搜索。基于DTW和基于HMM的语音识别系统中的匹配过程被视为通过受约束节点搜索最优路径的问题。图搜索算法在语音识别中的应用中，有效的搜索算法有两种，即波束搜索算法和最佳优先搜索算法。使用波束搜索算法的语音识别中的传统修剪策略仅基于从开始节点到当前节点的分数。不使用从当前节点到终端节点的分数估计。使用最佳优先搜索算法将 A^* 算法引入语音识别。本报告通过从图搜索的角度对匹配过程进行建模，描述了基于 DTW 和基于 HMM 的语音识别算法的新方法。第一章描述了一种利用波束搜索算法的基于DTW的语音识别。第二章描述了一种利用最佳优先搜索算法的基于DTW的语音识别。最后在第三章中，描述了一种利用最佳优先搜索算法的基于HMM的语音识别。