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

Automated structure discovery and parameter tuning of neural network language model based on evolution strategy 基于进化策略的神经网络语言模型自动结构发现与参数整定

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846334

Tomohiro Tanaka, Takafumi Moriya, T. Shinozaki, Shinji Watanabe, Takaaki Hori, Kevin Duh

引用次数: 16

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

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

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

Parallel Long Short-Term Memory for multi-stream classification 多流分类的并行长短期记忆

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846268

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

{"title":"Parallel Long Short-Term Memory for multi-stream classification","authors":"Mohamed Bouaziz, Mohamed Morchid, Richard Dufour, G. Linarès, R. Mori","doi":"10.1109/SLT.2016.7846268","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846268","url":null,"abstract":"Recently, machine learning methods have provided a broad spectrum of original and efficient algorithms based on Deep Neural Networks (DNN) to automatically predict an outcome with respect to a sequence of inputs. Recurrent hidden cells allow these DNN-based models to manage long-term dependencies such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM). Nevertheless, these RNNs process a single input stream in one (LSTM) or two (Bidirectional LSTM) directions. But most of the information available nowadays is from multistreams or multimedia documents, and require RNNs to process these information synchronously during the training. This paper presents an original LSTM-based architecture, named Parallel LSTM (PLSTM), that carries out multiple parallel synchronized input sequences in order to predict a common output. The proposed PLSTM method could be used for parallel sequence classification purposes. The PLSTM approach is evaluated on an automatic telecast genre sequences classification task and compared with different state-of-the-art architectures. Results show that the proposed PLSTM method outperforms the baseline n-gram models as well as the state-of-the-art LSTM approach.","PeriodicalId":281635,"journal":{"name":"2016 IEEE Spoken Language Technology Workshop (SLT)","volume":"113 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":"126700902","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: 11

BBN technologies' OpenSAD system BBN technologies的OpenSAD系统

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846238

Scott Novotney, D. Karakos, J. Silovský, R. Schwartz

引用次数: 2

Automated optimization of decoder hyper-parameters for online LVCSR 在线LVCSR解码器超参数的自动优化

2016 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2016-12-01 DOI: 10.1109/SLT.2016.7846303

Akshay Chandrashekaran, Ian Lane

{"title":"Automated optimization of decoder hyper-parameters for online LVCSR","authors":"Akshay Chandrashekaran, Ian Lane","doi":"10.1109/SLT.2016.7846303","DOIUrl":"https://doi.org/10.1109/SLT.2016.7846303","url":null,"abstract":"In this paper, we explore the usage of automated hyper-parameter optimization techniques with scalarization of multiple objectives to find decoder hyper-parameters suitable for a given acoustic and language model for an LVCSR task. We compare manual optimization, random sampling, tree of Parzen estimators, Bayesian Optimization, and genetic algorithm to find a technique that yields better performance than manual optimization in a comparable number of hyper-parameter evaluations. We focus on a scalar combination of word error rate (WER), log of real time factor (logRTF), and peak memory usage, formulated using the augmented Tchebyscheff function(ATF), as the objective function for the automated techniques. For this task, with a constraint on the maximum number of objective evaluations, we find that the best automated optimization technique: Bayesian Optimization outperforms manual optimization by 8% in terms of ATF. We find that memory usage was not a very useful distinguishing factor between different hyper-parameter settings, with trade-offs occurring between RTF and WER a majority of the time. We also try to perform optimization of WER with a hard constraint on the real time factor of 0.1. In this case, performing constrained Bayesian Optimization yields a model that provides an improvement of 2.7% over the best model obtained from manual optimization with 60% the number of evaluations.","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":"125964616","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: 7