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。无监督的Acouth声学和语言模型的方法可以使自发日语（CSJ）R系统具有全稳态，作为声学模型，识别结果显示出识别结果的降低。通过无监督的适应性，通过加权适应数据的基础，我们还比较了Cotinuul-Mixture Fram（CHMM）系统和离散混合HMM（DMHMM）的性能，DMHMM系统几乎提供了与CHMM系统的相同性能，并且使用了6000台24个状态的DMHMMS获得了19.73％的功能，比CHMM的dmhmms更大。基于离散混合HMM的嘈杂条件下的稳健语音方法（DMHMMS）。对于直方图均衡（HEQ）的DMHMS。使用HEQ的DMHMS在模型空间归一化中，DMHMMS的代码簿通过HEQ方法得出的转换函数。强大的语音识别

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

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

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

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