2009 IEEE Workshop on Automatic Speech Recognition & Understanding最新文献_第9页

Improved decision trees for multi-stream HMM-based audio-visual continuous speech recognition 基于多流hmm的视听连续语音识别改进决策树

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373454

Jing Huang, Karthik Venkat Ramanan

引用次数: 3

Constrained discriminative training of N-gram language models N-gram语言模型的约束判别训练

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373338

A. Rastrow, A. Sethy, B. Ramabhadran

{"title":"Constrained discriminative training of N-gram language models","authors":"A. Rastrow, A. Sethy, B. Ramabhadran","doi":"10.1109/ASRU.2009.5373338","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373338","url":null,"abstract":"In this paper, we present a novel version of discriminative training for N-gram language models. Language models impose language specific constraints on the acoustic hypothesis and are crucial in discriminating between competing acoustic hypotheses. As reported in the literature, discriminative training of acoustic models has yielded significant improvements in the performance of a speech recognition system, however, discriminative training for N-gram language models (LMs) has not yielded the same impact. In this paper, we present three techniques to improve the discriminative training of LMs, namely updating the back-off probability of unseen events, normalization of the N-gram updates to ensure a probability distribution and a relative-entropy based global constraint on the N-gram probability updates. We also present a framework for discriminative adaptation of LMs to a new domain and compare it to existing linear interpolation methods. Results are reported on the Broadcast News and the MIT lecture corpora. A modest improvement of 0.2% absolute (on Broadcast News) and 0.3% absolute (on MIT lectures) was observed with discriminatively trained LMs over state-of-the-art systems.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"416 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122461206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Automatic punctuation generation for speech 语音自动标点生成

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373365

Wenzhu Shen, Roger Peng Yu, F. Seide, Ji Wu

引用次数: 5

Robust Speaker Diarization for short speech recordings 稳健的说话人拨号短语音记录

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373254

David Imseng, G. Friedland

引用次数: 30

Reinforcing language model for speech translation with auxiliary data 用辅助数据强化语音翻译的语言模型

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373308

Jia Cui, Yonggang Deng, Bowen Zhou

引用次数: 1

Spoken term detection from bilingual spontaneous speech using code-switched lattice-based structures for words and subword units 基于码交换格结构的词和子词单元的双语自发语音词汇检测

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5372901

Hung-yi Lee, Yueh-Lien Tang, Hao Tang, Lin-Shan Lee

引用次数: 8

Discriminative Product-of-Expert acoustic mapping for cross-lingual phone recognition 跨语言电话识别的专家判别产品声学映射

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5372910

K. Sim

{"title":"Discriminative Product-of-Expert acoustic mapping for cross-lingual phone recognition","authors":"K. Sim","doi":"10.1109/ASRU.2009.5372910","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5372910","url":null,"abstract":"This paper presents a Product-of-Expert framework to perform probabilistic acoustic mapping for cross-lingual phone recognition. Under this framework, the posterior probabilities of the target HMM states are modelled as the weighted product of experts, where the experts or their weights are modelled as functions of the posterior probabilities of the source HMM states generated by a foreign phone recogniser. Careful choice of these functions leads to the Product-of-Posterior and Posterior Weighted Product-of-Expert models, which can be conveniently represented as 2-layer and 3-layer feed-forward neural networks respectively. Therefore, the commonly used error back-propagation method can be used to discriminatively train the model parameters. Experimental results are presented on the NTIMIT database using the Czech, Hungarian and Russian hybrid NN/HMM recognisers as the foreign phone recognisers to recognise English phones. With only about 15.6 minutes of training data, the best acoustic mapping model achieved 46.00% phone error rate, which is not far behind the 43.55% performance of the NN/HMM system trained directly on the full 3.31 hours of data.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116106947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 35

A study on hidden Markov model's generalization capability for speech recognition 隐马尔可夫模型在语音识别中的泛化能力研究

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373359

Xiong Xiao, Jinyu Li, Chng Eng Siong, Haizhou Li, Chin-Hui Lee

{"title":"A study on hidden Markov model's generalization capability for speech recognition","authors":"Xiong Xiao, Jinyu Li, Chng Eng Siong, Haizhou Li, Chin-Hui Lee","doi":"10.1109/ASRU.2009.5373359","DOIUrl":"https://doi.org/10.1109/ASRU.2009.5373359","url":null,"abstract":"From statistical learning theory, the generalization capability of a model is the ability to generalize well on unseen test data which follow the same distribution as the training data. This paper investigates how generalization capability can also improve robustness when testing and training data are from different distributions in the context of speech recognition. Two discriminative training (DT) methods are used to train the hidden Markov model (HMM) for better generalization capability, namely the minimum classification error (MCE) and the soft-margin estimation (SME) methods. Results on Aurora-2 task show that both SME and MCE are effective in improving one of the measures of acoustic model's generalization capability, i.e. the margin of the model, with SME be moderately more effective. In addition, the better generalization capability translates into better robustness of speech recognition performance, even when there is significant mismatch between the training and testing data. We also applied the mean and variance normalization (MVN) to preprocess the data to reduce the training-testing mismatch. After MVN, MCE and SME perform even better as the generalization capability now is more closely related to robustness. The best performance on Aurora-2 is obtained from SME and about 28% relative error rate reduction is achieved over the MVN baseline system. Finally, we also use SME to demonstrate the potential of better generalization capability in improving robustness in more realistic noisy task using the Aurora-3 task, and significant improvements are obtained.","PeriodicalId":292194,"journal":{"name":"2009 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123938620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Towards the use of inferred cognitive states in language modeling 论在语言建模中使用推断认知状态

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373290

Nigel G. Ward, Alejandro Vega

引用次数: 8

Three-layer optimizations for fast GMM computations on GPU-like parallel processors 在类似gpu的并行处理器上进行快速GMM计算的三层优化

2009 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2009-12-01 DOI: 10.1109/ASRU.2009.5373410

Kshitij Gupta, John Douglas Owens

引用次数: 21