2016 IEEE Spoken Language Technology Workshop (SLT)最新文献_第2页

Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features 通过语音和词汇特征的层次融合来识别口语对话中的情绪

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846319

Leimin Tian, Johanna D. Moore, Catherine Lai

{"title":"Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features","authors":"Leimin Tian, Johanna D. Moore, Catherine Lai","doi":"10.1109/SLT.2016.7846319","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846319","url":null,"abstract":"Automatic emotion recognition is vital for building natural and engaging human-computer interaction systems. Combining information from multiple modalities typically improves emotion recognition performance. In previous work, features from different modalities have generally been fused at the same level with two types of fusion strategies: Feature-Level fusion, which concatenates feature sets before recognition; and Decision-Level fusion, which makes the final decision based on outputs of the unimodal models. However, different features may describe data at different time scales or have different levels of abstraction. Cognitive Science research also indicates that when perceiving emotions, humans use information from different modalities at different cognitive levels and time steps. Therefore, we propose a Hierarchical fusion strategy for multimodal emotion recognition, which incorporates global or more abstract features at higher levels of its knowledge-inspired structure. We build multimodal emotion recognition models combining state-of-the-art acoustic and lexical features to study the performance of the proposed Hierarchical fusion. Experiments on two emotion databases of spoken dialogue show that this fusion strategy consistently outperforms both Feature-Level and Decision-Level fusion. The multimodal emotion recognition models using the Hierarchical fusion strategy achieved state-of-the-art performance on recognizing emotions in both spontaneous and acted dialogue.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124895494","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: 45

Improved prediction of the accent gap between speakers of English for individual-based clustering of World Englishes 改进了基于个体的世界英语聚类的英语说话者之间的口音差距预测

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846255

Fumiya Shiozawa, D. Saito, N. Minematsu

{"title":"Improved prediction of the accent gap between speakers of English for individual-based clustering of World Englishes","authors":"Fumiya Shiozawa, D. Saito, N. Minematsu","doi":"10.1109/SLT.2016.7846255","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846255","url":null,"abstract":"The term of “World Englishes” describes the current state of English and one of their main characteristics is a large diversity of pronunciation, called accents. In our previous studies, we developed several techniques to realize effective clustering and visualization of the diversity. For this aim, the accent gap between two speakers has to be quantified independently of extra-linguistic factors such as age and gender. To realize this, a unique representation of speech, called speech structure, which is theoretically invariant against these factors, was applied to represent pronunciation. In the current study, by controlling the degree of invariance, we attempt to improve accent gap prediction. Two techniques are tested: DNN-based model-free estimation of divergence and multi-stream speech structures. In the former, instead of estimating separability between two speech events based on some model assumptions, DNN-based class posteriors are utilized for estimation. In the latter, by deriving one speech structure for each sub-space of acoustic features, constrained invariance is realized. Our proposals are tested in terms of the correlation between reference accent gaps and the predicted and quantified gaps. Experiments show that the correlation is improved from 0.718 to 0.730.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129088224","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

A log-linear weighting approach in the Word2vec space for spoken language understanding 用于口语理解的Word2vec空间的对数线性加权方法

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846289

Killian Janod, Mohamed Morchid, Richard Dufour, G. Linarès

引用次数: 3

Improving multi-stream classification by mapping sequence-embedding in a high dimensional space 利用高维空间映射序列嵌入改进多流分类

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846269

Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, G. Linarès

{"title":"Improving multi-stream classification by mapping sequence-embedding in a high dimensional space","authors":"Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, G. Linarès","doi":"10.1109/SLT.2016.7846269","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846269","url":null,"abstract":"Most of the Natural and Spoken Language Processing tasks now employ Neural Networks (NN), allowing them to reach impressive performances. Embedding features allow the NLP systems to represent input vectors in a latent space and to improve the observed performances. In this context, Recurrent Neural Network (RNN) based architectures such as Long Short-Term Memory (LSTM) are well known for their capacity to encode sequential data into a non-sequential hidden vector representation, called sequence embedding. In this paper, we propose an LSTM-based multi-stream sequence embedding in order to encode parallel sequences by a single non-sequential latent representation vector. We then propose to map this embedding representation in a high-dimensional space using a Support Vector Machine (SVM) in order to classify the multi-stream sequences by finding out an optimal hyperplane. Multi-stream sequence embedding allowed the SVM classifier to more efficiently profit from information carried by both parallel streams and longer sequences. The system achieved the best performance, in a multi-stream sequence classification task, with a gain of 9 points in error rate compared to an SVM trained on the original input sequences.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114891322","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

Automatic optimization of data perturbation distributions for multi-style training in speech recognition 语音识别中多风格训练数据扰动分布的自动优化

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846240

Mortaza Doulaty, R. Rose, O. Siohan

{"title":"Automatic optimization of data perturbation distributions for multi-style training in speech recognition","authors":"Mortaza Doulaty, R. Rose, O. Siohan","doi":"10.1109/SLT.2016.7846240","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846240","url":null,"abstract":"Speech recognition performance using deep neural network based acoustic models is known to degrade when the acoustic environment and the speaker population in the target utterances are significantly different from the conditions represented in the training data. To address these mismatched scenarios, multi-style training (MTR) has been used to perturb utterances in an existing uncorrupted and potentially mismatched training speech corpus to better match target domain utterances. This paper addresses the problem of determining the distribution of perturbation levels for a given set of perturbation types that best matches the target speech utterances. An approach is presented that, given a small set of utterances from a target domain, automatically identifies an empirical distribution of perturbation levels that can be applied to utterances in an existing training set. Distributions are estimated for perturbation types that include acoustic background environments, reverberant room configurations, and speaker related variation like frequency and temporal warping. The end goal is for the resulting perturbed training set to characterize the variability in the target domain and thereby optimize ASR performance. An experimental study is performed to evaluate the impact of this approach on ASR performance when the target utterances are taken from a simulated far-field acoustic environment.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114923030","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: 9

Unsupervised context learning for speech recognition 语音识别的无监督上下文学习

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846302

A. Michaely, M. Ghodsi, Zelin Wu, Justin Scheiner, Petar S. Aleksic

引用次数: 10

Deep learning with maximal figure-of-merit cost to advance multi-label speech attribute detection 基于最优值代价的深度学习推进多标签语音属性检测

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846308

Ivan Kukanov, Ville Hautamäki, S. Siniscalchi, Kehuang Li

引用次数: 8

An unsupervised vocabulary selection technique for Chinese automatic speech recognition 中文语音自动识别的无监督词汇选择技术

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846298

Yike Zhang, Pengyuan Zhang, Ta Li, Yonghong Yan

{"title":"An unsupervised vocabulary selection technique for Chinese automatic speech recognition","authors":"Yike Zhang, Pengyuan Zhang, Ta Li, Yonghong Yan","doi":"10.1109/SLT.2016.7846298","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846298","url":null,"abstract":"The vocabulary is a vital component of automatic speech recognition(ASR) systems. For a specific Chinese speech recognition task, using a large general vocabulary not only leads to a much longer time to decode, but also hurts the recognition accuracy. In this paper, we proposed an unsupervised algorithm to select task-specific words from a large general vocabulary. The out-of-vocabulary(OOV) rate is a measure of vocabularies, and it is related to the recognition accuracy. However, it is hard to compute OOV rate for a Chinese vocabulary, since OOVs are often segmented into single Chinese characters and most Chinese vocabularies contain all the single Chinese characters. To deal with this problem, we proposed a novel method to estimate the OOV rate of Chinese vocabularies. In experiments, we found that our estimated OOV rate is related to the character error rate(CER) of recognition. Our proposed vocabulary selection method provided both the lowest OOV rate and CER on two Chinese conversational telephone speech(CTS) evaluation sets compared to the general vocabulary and frequency based vocabulary selection method. In addition, our proposed method significantly reduced the size of the language model(LM) and the corresponding weighted finite state transducer(WFST) network, which led to a more efficient decoding.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123135691","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

Semantic model for fast tagging of word lattices 快速标注词格的语义模型

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846295

L. Velikovich

引用次数: 7

Environmentally robust audio-visual speaker identification 环境稳健的视听扬声器识别

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846282

Lea Schonherr, Dennis Orth, M. Heckmann, D. Kolossa

引用次数: 9