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2011 IEEE Workshop on Automatic Speech Recognition & Understanding最新文献

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Designing text corpus using phone-error distribution for acoustic modeling 利用电话误差分布设计文本语料库进行声学建模
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163929
H. Murakami, K. Shinoda, S. Furui
{"title":"Designing text corpus using phone-error distribution for acoustic modeling","authors":"H. Murakami, K. Shinoda, S. Furui","doi":"10.1109/ASRU.2011.6163929","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163929","url":null,"abstract":"It is expensive to prepare a sufficient amount of training data for acoustic modeling for developing large vocabulary continuous speech recognition systems. This is a serious problem especially for resource-deficient languages. We propose an active learning method that effectively reduces the amount of training data without any degradation in recognition performance. It is used to design a text corpus for read speech collection. It first estimates phone-error distribution using a small amount of fully transcribed speech data. Second, it constructs a sentence set whose phone-occurrence distribution is close to the phone-error distribution and collects its speech data. It then extends this process to diphones and triphones and collects more speech data. We evaluated our method with simulation experiments using the Corpus of Spontaneous Japanese. It required only 76 h of speech data to achieve word accuracy of 74.7%, while the conventional training method required 152 h of data to achieve the same rate.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124668654","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: 2
Blind noise suppression for Non-Audible Murmur recognition with stereo signal processing 用立体声信号处理盲噪声抑制非听杂音识别
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163981
Shunta Ishii, T. Toda, H. Saruwatari, S. Sakti, Satoshi Nakamura
{"title":"Blind noise suppression for Non-Audible Murmur recognition with stereo signal processing","authors":"Shunta Ishii, T. Toda, H. Saruwatari, S. Sakti, Satoshi Nakamura","doi":"10.1109/ASRU.2011.6163981","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163981","url":null,"abstract":"In this paper, we propose a blind noise suppression method for Non-Audible Murmur (NAM) recognition. NAM is a very soft whispered voice detected with NAM microphone, which is one of the body-conductive microphones. Due to its recording mechanism, the detected signal suffers from noise caused by speaker's movements. In the proposed method using a stereo signal detected with two NAM microphones, the noise is estimated with blind source separation, and then, spectral subtraction is performed in each channel to reduce the noise. Moreover, channel selection is performed frame by frame to generate less distorted monaural NAM signal. Experimental results show that 1) word accuracy in large vocabulary continuous NAM recognition is degraded from 69.2% to 53.6% by the noise and 2) it is significantly recovered to 63.3% in a simulated situation and 58.6% in a real situation with the proposed method.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"44 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120908338","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: 6
Convolutive Bottleneck Network features for LVCSR LVCSR的卷积瓶颈网络特征
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163903
Karel Veselý, M. Karafiát, F. Grézl
{"title":"Convolutive Bottleneck Network features for LVCSR","authors":"Karel Veselý, M. Karafiát, F. Grézl","doi":"10.1109/ASRU.2011.6163903","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163903","url":null,"abstract":"In this paper, we focus on improvements of the bottleneck ANN in a Tandem LVCSR system. First, the influence of training set size and the ANN size is evaluated. Second, a very positive effect of linear bottleneck is shown. Finally a Convolutive Bottleneck Network is proposed as extension of the current state-of-the-art Universal Context Network. The proposed training method leads to 5.5% relative reduction of WER, compared to the Universal Context ANN baseline. The relative improvement compared to the 5-layer single-bottleneck network is 17.7%. The dataset ctstrain07 composed of more than 2000 hours of English Conversational Telephone Speech was used for the experiments. The TNet toolkit with CUDA GPGPU implementation was used for fast training.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121226803","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: 108
Automatic detection of “g-dropping” in American English using forced alignment 使用强制对齐自动检测美式英语中的“g-drop”
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163980
Jiahong Yuan, M. Liberman
{"title":"Automatic detection of “g-dropping” in American English using forced alignment","authors":"Jiahong Yuan, M. Liberman","doi":"10.1109/ASRU.2011.6163980","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163980","url":null,"abstract":"This study investigated the use of forced alignment for automatic detection of “g-dropping” in American English (e.g., walkin'). Two acoustic models were trained, one for -in' and the other for -ing. The models were added to the Penn Phonetics Lab Forced Aligner, and forced alignment will choose the more probable pronunciation from the two alternatives. The agreement rates between the forced alignment method and native English speakers ranged from 79% to 90%, which were comparable to the agreement rates among the native speakers (79% – 96%). The two variations of pronunciation not only differed in their nasal codas, but also - and even more so - in their vowel quality. This is shown by both the KL-divergence between the two models, and that native Mandarin speakers performed poorly on classification of “g-dropping”.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130380235","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: 30
Sentiment analysis of text-to-speech input using latent affective mapping 基于潜在情感映射的文本-语音输入情感分析
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163948
J. Bellegarda
{"title":"Sentiment analysis of text-to-speech input using latent affective mapping","authors":"J. Bellegarda","doi":"10.1109/ASRU.2011.6163948","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163948","url":null,"abstract":"To impart a congruent emotional quality to synthetic speech, it is expedient to leverage the overall polarity of the input text. This is feasible inasmuch as speech generation complies with the outcome of sentiment analysis. We have recently introduced latent affective mapping [1]–[3], a new approach to emotion detection which exploits two separate levels of semantic information: one that encapsulates the foundations of the domain considered, and one that specifically accounts for the overall affective fabric of the language. The ensuing framework exposes the emergent relationship between these two levels in order to advantageously inform affective evaluation. This paper applies latent affective mapping to the narrower problem of sentiment analysis, in order to achieve a more robust identification of the polarity of textual data. Empirical evidence gathered on the “Affective Text” portion of the SemEval-2007 corpus [4] shows that this approach is promising for automatic sentiment prediction in text. This bodes well as a first step in ensuring emotional congruence in text-to-speech synthesis.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131185814","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: 1
Topic modeling for spoken documents using only phonetic information 仅使用语音信息的口语文档主题建模
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163964
Timothy J. Hazen, M. Siu, H. Gish, S. Lowe, Arthur Chan
{"title":"Topic modeling for spoken documents using only phonetic information","authors":"Timothy J. Hazen, M. Siu, H. Gish, S. Lowe, Arthur Chan","doi":"10.1109/ASRU.2011.6163964","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163964","url":null,"abstract":"This paper explores both supervised and unsupervised topic modeling for spoken audio documents using only phonetic information. In cases where word-based recognition is unavailable or infeasible, phonetic information can be used to indirectly learn and capture information provided by topically relevant lexical items. In some situations, a lack of transcribed data can prevent supervised training of a same-language phonetic recognition system. In these cases, phonetic recognition can use cross-language models or self-organizing units (SOUs) learned in a completely unsupervised fashion. This paper presents recent improvements in topic modeling using only phonetic information. We present new results using recently developed techniques for discriminative training for topic identification used in conjunction with recent improvements in SOU learning. A preliminary examination of the use of unsupervised latent topic modeling for unsupervised discovery of topics and topically relevant lexical items from phonetic information is also presented.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134463728","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: 19
Automatic detection of unnatural word-level segments in unit-selection speech synthesis 单位选择语音合成中非自然词段的自动检测
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163946
William Yang Wang, Kallirroi Georgila
{"title":"Automatic detection of unnatural word-level segments in unit-selection speech synthesis","authors":"William Yang Wang, Kallirroi Georgila","doi":"10.1109/ASRU.2011.6163946","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163946","url":null,"abstract":"We investigate the problem of automatically detecting unnatural word-level segments in unit selection speech synthesis. We use a large set of features, namely, target and join costs, language models, prosodic cues, energy and spectrum, and Delta Term Frequency Inverse Document Frequency (TF-IDF), and we report comparative results between different feature types and their combinations. We also compare three modeling methods based on Support Vector Machines (SVMs), Random Forests, and Conditional Random Fields (CRFs). We then discuss our results and present a comprehensive error analysis.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132878894","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: 15
A convex hull approach to sparse representations for exemplar-based speech recognition 基于范例的语音识别稀疏表示的凸包方法
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163906
Tara N. Sainath, D. Nahamoo, D. Kanevsky, B. Ramabhadran, P. Shah
{"title":"A convex hull approach to sparse representations for exemplar-based speech recognition","authors":"Tara N. Sainath, D. Nahamoo, D. Kanevsky, B. Ramabhadran, P. Shah","doi":"10.1109/ASRU.2011.6163906","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163906","url":null,"abstract":"In this paper, we propose a novel exemplar based technique for classification problems where for every new test sample the classification model is re-estimated from a subset of relevant samples of the training data.We formulate the exemplar-based classification paradigm as a sparse representation (SR) problem, and explore the use of convex hull constraints to enforce both regularization and sparsity. Finally, we utilize the Extended Baum-Welch (EBW) optimization technique to solve the SR problem. We explore our proposed methodology on the TIMIT phonetic classification task, showing that our proposed method offers statistically significant improvements over common classification methods, and provides an accuracy of 82.9%, the best single-classifier number reported to date.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115222749","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: 12
A novel neural-based pronunciation modeling method for robust speech recognition 鲁棒语音识别中一种新的基于神经网络的语音建模方法
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163985
Guangpu Huang, M. Er
{"title":"A novel neural-based pronunciation modeling method for robust speech recognition","authors":"Guangpu Huang, M. Er","doi":"10.1109/ASRU.2011.6163985","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163985","url":null,"abstract":"This paper describes a recurrent neural network (RNN) based articulatory-phonetic inversion (API) model for improved speech recognition. And a specialized optimization algorithm is introduced to enable human-like heuristic learning in an efficient data-driven manner to capture the dynamic nature of English speech pronunciations. The API model demonstrates superior pronunciation modeling ability and robustness against noise contaminations in large-vocabulary speech recognition experiments. Using a simple rescoring formula, it improves the hidden Markov model (HMM) baseline speech recognizer with consistent error rates reduction of 5.30% and 10.14% for phoneme recognition tasks on clean and noisy speech respectively on the selected TIMIT datasets. And an error rate reduction of 3.35% is obtained for the SCRIBE-TIMIT word recognition tasks. The proposed system qualifies as a competitive candidate for profound pronunciation modeling with intrinsic salient features such as generality and portability.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121214557","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: 3
Frame-level AnyBoost for LVCSR with the MMI Criterion 具有MMI标准的LVCSR的帧级AnyBoost
2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163897
Ryuki Tachibana, Takashi Fukuda, U. Chaudhari, B. Ramabhadran, P. Zhan
{"title":"Frame-level AnyBoost for LVCSR with the MMI Criterion","authors":"Ryuki Tachibana, Takashi Fukuda, U. Chaudhari, B. Ramabhadran, P. Zhan","doi":"10.1109/ASRU.2011.6163897","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163897","url":null,"abstract":"This paper propose a variant of AnyBoost for a large vocabulary continuous speech recognition (LVCSR) task. AnyBoost is an efficient algorithm to train an ensemble of weak learners by gradient descent for an objective function.We present a novel training procedure that trains acoustic models via the MMI criterion using data that is weighted proportional to the summation of the posterior functions of previous round of weak learners. Optimized for system combination by n-best ROVER at runtime, data weights for a new weak learner are computed as a weighted summation of posteriors of previous weak learners. We compare a frame-based version and a sentence-based version of our proposed algorithm with a frame-based AdaBoost algorithm. We will present results on a voice search task trained with different amounts of data with gains of 5.1% to 7.5% relative in WER can be obtained by three rounds of boosting.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121487344","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: 1
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