Large-vocabulary continuous speech recognition on spontaneous speech task

自发语音任务的大词汇量连续语音识别

• 批准号: 18500126
• 负责人: KOHDA Masaki
• 金额: $ 1.22万
• 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: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18500126/
• 关键词: Corpus of Spontaneous Japanese; Speech recognition; Acoustic model; Language model; Unsupervised adaptation; System integration; Robust speech recognition; 混合連続分布HMM; 離散混合分布HMM

1. Large-vocabulary continuous speech recognition on spontaneous speech taskIn large-vocabulary continuous speech recognition, we investigate several methods of unsupervised adaptation of both acoustic and language models and evaluate the methods on the Corpus of Spontaneous Japanese (CSJ). The LVCSR system has full-covariance matrices as the acoustic model. The results of recognition experiments showed the decrease in word error rate (WER) from 19.17% without adaptation to 14.73% with unsupervised adaptation, moreover to 14.47% with unsupervised adaptation by weighting the adaptation data on the basis of a part of speech. Also, we compared the performance between continuous-mixture FRAM (CHMM) system and discrete-mixture HMM (DMHMM) system on the CSJ. As a result, DMHMM system provided almost the same performance as the CHMM system and WER of 19.73% had been obtained with 6000-state 24-mixture DMHMMs, though it has been generally believed that the recognition error rates of DMHMM were … More much higher than those of CHMM until now.2. Robust speech recognition using discrete-mixture HMMsWe introduce a new method of robust speech recognition under noisy conditions based on discrete-mixture HMMs (DMHMMs). DMHMMs were originally proposed to reduce calculation costs in the decoding process. Recently, we have applied DMHMMs to noisy speech recognition, and found that they were effective for modeling noisy speech. Towards the further improvement of noise-robust speech recognition, we propose a novel normalization method for DMHMMs based on histogram equalization (HEQ). The HEQ method can compensate the nonlinear effects of additive noise. It is generally used for the feature space normalization of continuous-mixture HMM (CHMM) systems. In this paper, we propose both model space and feature space normalization of DMHMMs by using HEQ. In the model space normalization, codebooks of DMHMMs are modified by the transform function derived from the HEQ method. The proposed method was compared using both conventional CHMMs and DMHMMs. The results showed that the model space normalization of DMHMMs by multiple transform functions was effective for noise-robust speech recognition. Less

1.在大词汇量连续语音识别中，我们研究了几种声学和语言模型的无监督自适应方法，并在自发日语语料库（CSJ）上对这些方法进行了评估。LVCSR系统具有全协方差矩阵作为声学模型。识别实验结果表明，通过对自适应数据按词性加权，自适应算法的误识率从未自适应的19.17%降低到无监督自适应的14.73%，进而降低到无监督自适应的14.47%。在CSJ上比较了连续混合弗拉姆（CHMM）系统和离散混合HMM（DMHMM）系统的性能。结果表明，DMHMM系统提供了几乎与CHMM系统相同的性能，并且在6000状态24混合DMHMM下获得了19.73%的WER，尽管人们普遍认为DMHMM的识别错误率是 ...更多信息 远远高于CHMM的水平.基于离散混合隐马尔可夫模型的鲁棒语音识别本文提出了一种基于离散混合隐马尔可夫模型（DMHMM）的噪声环境下鲁棒语音识别方法。DMHHT最初被提出来减少解码过程中的计算成本。最近，我们已经将DMHHT应用于含噪语音识别，并发现它们对含噪语音建模是有效的。为了进一步提高语音识别的抗噪性，提出了一种基于直方图均衡化（HEQ）的DMH归一化方法。HEQ方法可以补偿加性噪声的非线性效应。它通常用于连续混合HMM（CHMM）系统的特征空间归一化。在本文中，我们提出了两个模型空间和特征空间规范化的DMHQs使用HEQ。在模型空间归一化中，利用HEQ方法推导出的变换函数对DMH码本进行修正。所提出的方法进行了比较，使用传统的CHCl 3和DMHCl 3。实验结果表明，采用多个变换函数对DMH进行模型空间归一化，对噪声鲁棒性语音识别是有效的。少

项目成果

話し言葉音声認識における教師なし適応の改善

改善口语语音识别中的无监督适应

• 发表时间: 2007
• 期刊: 情報処理学会東北支部研究会 06-6-A1-3
• 作者: Hiroyuki;Narita;Yasumasa;Sawamura;Akira;Hayashi;渥美雅保;Masayasu Atsumi;草間隆
• 通讯作者: 草間隆

マルチコンディションモデルを用いた音楽環境下の音声認識の検討

基于多条件模型的音乐环境下语音识别研究

• 发表时间: 2008
• 作者: Y. Takeda;M. Katoh;T. Kosaka;M. Kohda;大貫芳久
• 通讯作者: 大貫芳久

Noisy Speech recognition Based on Codebook Normalization of Discrete-Mixture HMMs

基于离散混合 HMM 码本归一化的噪声语音识别

• 发表时间: 2006
• 期刊: ASA/ASJ Forth Joint Meeting 1pSC27
• 作者: T.Kosaka;M.Katoh;M.Kohda
• 通讯作者: M.Kohda

Spontaneous speech recognition using discrete-mixture HMMs

使用离散混合 HMM 的自发语音识别

• 发表时间: 2007
• 作者: T. Kosaka;M. Katoh;M. Kohda
• 通讯作者: M. Kohda

Robust Speech Recognition and Understanding

强大的语音识别和理解

• 发表时间: 2007
• 作者: Tetsuya Takiguchi;R. Takashima;Y. Ariki;Hironori Matsumasa;Hyunsin Park;Tetsuya Takiguchi;高島遼一;高島遼一;高島遼一;室井貴司;吉井麻里子;室井貴司;三宅信之;室井貴司;三宅信之;朴玄信;三宅信之;高島遼一;朴玄信;室井貴司;松田博義;住田雄司;高島遼一;朴玄信;松田博義;三宅信之;住田雄司;松田博義;三宅信之;Tetsuya Takiguchi;Tetsuya Takiguchi
• 通讯作者: Tetsuya Takiguchi