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

Matched-condition robust Dynamic Noise Adaptation 匹配条件鲁棒动态噪声自适应

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163919

Steven J. Rennie, Pierre L. Dognin, P. Fousek

引用次数: 5

Efficient spoken term discovery using randomized algorithms 使用随机化算法的高效口语术语发现

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163965

A. Jansen, Benjamin Van Durme

引用次数: 165

Improved spoken term detection using support vector machines with acoustic and context features from pseudo-relevance feedback 利用基于伪相关反馈的声学和上下文特征的支持向量机改进口语术语检测

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163962

Tsung-wei Tu, Hung-yi Lee, Lin-Shan Lee

引用次数: 18

Applying feature bagging for more accurate and robust automated speaking assessment 应用特征套袋更准确和强大的自动说话评估

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163977

L. Chen

引用次数: 1

A factored conditional random field model for articulatory feature forced transcription 发音特征强制转录的因子条件随机场模型

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163909

Rohit Prabhavalkar, E. Fosler-Lussier, Karen Livescu

引用次数: 12

Exploiting distance based similarity in topic models for user intent detection 利用主题模型中基于距离的相似度进行用户意图检测

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163969

Asli Celikyilmaz, Dilek Z. Hakkani-Tür, Gökhan Tür, Ashley Fidler, D. Hillard

引用次数: 13

Estimating document frequencies in a speech corpus 估计语料库中的文档频率

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163966

D. Karakos, Mark Dredze, K. Church, A. Jansen, S. Khudanpur

{"title":"Estimating document frequencies in a speech corpus","authors":"D. Karakos, Mark Dredze, K. Church, A. Jansen, S. Khudanpur","doi":"10.1109/ASRU.2011.6163966","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163966","url":null,"abstract":"Inverse Document Frequency (IDF) is an important quantity in many applications, including Information Retrieval. IDF is defined in terms of document frequency, df (w), the number of documents that mention w at least once. This quantity is relatively easy to compute over textual documents, but spoken documents are more challenging. This paper considers two baselines: (1) an estimate based on the 1-best ASR output and (2) an estimate based on expected term frequencies computed from the lattice. We improve over these baselines by taking advantage of repetition. Whatever the document is about is likely to be repeated, unlike ASR errors, which tend to be more random (Poisson). In addition, we find it helpful to consider an ensemble of language models. There is an opportunity for the ensemble to reduce noise, assuming that the errors across language models are relatively uncorrelated. The opportunity for improvement is larger when WER is high. This paper considers a pairing task application that could benefit from improved estimates of df. The pairing task inputs conversational sides from the English Fisher corpus and outputs estimates of which sides were from the same conversation. Better estimates of df lead to better performance on this task.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"7 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":"132137021","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: 13

Minimum detection error training of subword detectors 子词检测器的最小检测误差训练

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163983

Alfonso M. Canterla, M. H. Johnsen

引用次数: 2

Cross-lingual portability of Chinese and english neural network features for French and German LVCSR 法语和德语LVCSR中英文神经网络特征的跨语言可移植性

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163960

Christian Plahl, R. Schlüter, H. Ney

{"title":"Cross-lingual portability of Chinese and english neural network features for French and German LVCSR","authors":"Christian Plahl, R. Schlüter, H. Ney","doi":"10.1109/ASRU.2011.6163960","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163960","url":null,"abstract":"This paper investigates neural network (NN) based cross-lingual probabilistic features. Earlier work reports that intra-lingual features consistently outperform the corresponding cross-lingual features. We show that this may not generalize. Depending on the complexity of the NN features, cross-lingual features reduce the resources used for training —the NN has to be trained on one language only— without any loss in performance w.r.t. word error rate (WER). To further investigate this inconsistency concerning intra- vs. cross-lingual neural network features, we analyze the performance of these features w.r.t. the degree of kinship between training and testing language, and the amount of training data used. Whenever the same amount of data is used for NN training, a close relationship between training and testing language is required to achieve similar results. By increasing the training data the relationship becomes less, as well as changing the topology of the NN to the bottle neck structure. Moreover, cross-lingual features trained on English or Chinese improve the best intra-lingual system for German up to 2% relative in WER and up to 3% relative for French and achieve the same improvement as for discriminative training. Moreover, we gain again up to 8% relative in WER by combining intra- and cross-lingual systems.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"3 5 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":"131272181","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: 37

Model-based parametric features for emotion recognition from speech 基于模型的语音情感识别参数特征

2011 IEEE Workshop on Automatic Speech Recognition & Understanding Pub Date : 2011-12-01 DOI: 10.1109/ASRU.2011.6163987

Sankaranarayanan Ananthakrishnan, Aravind Namandi Vembu, R. Prasad

{"title":"Model-based parametric features for emotion recognition from speech","authors":"Sankaranarayanan Ananthakrishnan, Aravind Namandi Vembu, R. Prasad","doi":"10.1109/ASRU.2011.6163987","DOIUrl":"https://doi.org/10.1109/ASRU.2011.6163987","url":null,"abstract":"Automatic emotion recognition from speech is desirable in many applications relying on spoken language processing. Telephone-based customer service systems, psychological healthcare initiatives, and virtual training modules are examples of real-world applications that would significantly benefit from such capability. Traditional utterance-level emotion recognition relies on a global feature set obtained by computing various statistics from raw segmental and supra-segmental measurements, including fundamental frequency (F0), energy, and MFCCs. In this paper, we propose a novel, model-based parametric feature set that better discriminates between the competing emotion classes. Our approach relaxes modeling assumptions associated with using global statistics (e.g. mean, standard deviation, etc.) of traditional segment-level features for classification, and results in significant improvements over the state-of-the-art in 7-way emotion classification accuracy on the standard, freely-available Berlin Emotional Speech Corpus. These improvements are consistent even in a reduced feature space obtained by Fisher's Multiple Linear Discriminant Analysis, demonstrating the signficantly higher discriminative power of the proposed feature set.","PeriodicalId":338241,"journal":{"name":"2011 IEEE Workshop on Automatic Speech Recognition & Understanding","volume":"45 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":"124559733","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