Spontaneous speech recognition

自发语音识别

基本信息

批准号：
15500098
负责人：
KOHDA Masaki
金额：
$ 2.05万
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：
2003
资助国家：
日本
起止时间：
2003 至 2005
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-15500098/
关键词：
Corpus of Spontaneous Japanese Spontaneous speech recognition Robust speech recognition Acoustic model Language model Unsupervised adaptation Continuous-mixture HMM Discrete-mixture HMM 音声認識発音変形依存モデル MLLR 品詞N-gram

项目摘要

We investigated spontaneous speech recognition on academic lecture task and obtained the following results.(1) Lecture speech recognition using pronunciation variant modeling and unsupervised adaptationWe focus on the pronunciation variations observed in spontaneous speech. Aiming to introduce the context-dependence of pronunciation variants, we propose a new method of language modeling based on morphological analysis data designed for pronunciation variant. The proposed method was evaluated on the Corpus of Spontaneous Japanese (CSJ) and achieved the decrease in word error rate (WER) by 4.74% absolute. In addition, unsupervised adaptation of both acoustic and language models was introduced to improve the recognition performance further. The results showed the decrease in WER from 19.96% without adaptation to 15.41% with unsupervised adaptation.(2) Lecture speech recognition using discrete-mixture HMMsWe have investigated noisy speech recognition by using discrete-mixture HMM (DMHMM), … More and found that the performance of DMHMM overcame that of continuous-mixture HMM under environmental noise conditions or impulsive noise conditions. However, it is not clear whether this method is effective in clean conditions. The aim of this investigation is to evaluate the performance of the DMHMM system in clean conditions. In evaluation, we decided to use the "Corpus of Spontaneous Japanese" (CSJ) because we want to compare the performance of our system with that of other recognition systems with common speech corpus, and clarify the performance in such a more difficult task. In the recognition experiments, 3000-state DMHMMs (16 mixture components per state) were used as acoustic models. The language model which represents the pronunciation variety was trained by using 6.86 million words from 2668 lectures in CSJ and was used for recognition. As a result, the system obtained 20.30% WER for 10 academic lectures uttered by male speakers and demonstrated the effectiveness of the proposed method. Less

我们在学术讲座任务上调查了赞助商喂养的语音识别，并获得了以下结果。（1）使用发音变体建模和无监督的Appationwe的讲座语音识别我们的重点是在赞助商喂养语音中观察到的发音变化。为了介绍发音变体的上下文依赖性，我们基于为发音变体设计的形态分析数据提出了一种新的语言建模方法。对所提出的方法进行了对自发日本（CSJ）语料库的评估，并使单词错误率（WER）的下降量减少了4.74％。此外，引入了声学模型和语言模型的无监督适应，以进一步提高识别性能。结果表明，在不接受监督的适应性的情况下，WER从19.96％下降至15.41％。（2）使用离散混合HMMSWE的讲座语音识别已通过使用离散混合HMM（DMHMM）（dmhmm）（更多）来调查噪声语音识别，更多，并且发现DMHMM过度噪音的情况下的噪声频率不足，以至于噪音不足。但是，尚不清楚这种方法在清洁条件下是否有效。这项投资的目的是评估在清洁条件下DMHMM系统的性能。在评估中，我们决定使用“自发日本的语料库”（CSJ），因为我们想将系统的性能与其他识别系统的性能与共同的语音语料库进行比较，并在如此艰巨的任务中阐明了性能。在识别实验中，将3000个状态的DMHMM（每个州的16个混合物组件）用作声学模型。代表发音品种的语言模型通过使用CSJ中的2668个讲座的686万字培训，并用于识别。结果，该系统在由男说话的10个学术讲座中获得了20.30％的速度，并证明了该方法的有效性。较少的