2012 IEEE Spoken Language Technology Workshop (SLT)最新文献_第5页

Recovery of acronyms, out-of-lattice words and pronunciations from parallel multilingual speech 从平行多语言语音中恢复首字母缩略词、格子外词和发音

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424248

João Miranda, J. Neto, A. Black

引用次数: 4

A critical analysis of two statistical spoken dialog systems in public use 对公共使用的两种统计口语对话系统的批判性分析

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424197

J. Williams

引用次数: 22

American sign language fingerspelling recognition with phonological feature-based tandem models 基于语音特征串联模型的美国手语手势语拼写识别

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424208

Taehwan Kim, Karen Livescu, Gregory Shakhnarovich

引用次数: 20

Using syntactic and confusion network structure for out-of-vocabulary word detection 利用句法和混淆网络结构进行词汇外词检测

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424215

Alex Marin, T. Kwiatkowski, Mari Ostendorf, Luke Zettlemoyer

引用次数: 21

Improving wideband speech recognition using mixed-bandwidth training data in CD-DNN-HMM 基于CD-DNN-HMM的混合带宽训练数据改进宽带语音识别

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424210

Jinyu Li, Dong Yu, J. Huang, Y. Gong

{"title":"Improving wideband speech recognition using mixed-bandwidth training data in CD-DNN-HMM","authors":"Jinyu Li, Dong Yu, J. Huang, Y. Gong","doi":"10.1109/SLT.2012.6424210","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424210","url":null,"abstract":"Context-dependent deep neural network hidden Markov model (CD-DNN-HMM) is a recently proposed acoustic model that significantly outperformed Gaussian mixture model (GMM)-HMM systems in many large vocabulary speech recognition (LVSR) tasks. In this paper we present our strategy of using mixed-bandwidth training data to improve wideband speech recognition accuracy in the CD-DNN-HMM framework. We show that DNNs provide the flexibility of using arbitrary features. By using the Mel-scale log-filter bank features we not only achieve higher recognition accuracy than using MFCCs, but also can formulate the mixed-bandwidth training problem as a missing feature problem, in which several feature dimensions have no value when narrowband speech is presented. This treatment makes training CD-DNN-HMMs with mixed-bandwidth data an easy task since no bandwidth extension is needed. Our experiments on voice search data indicate that the proposed solution not only provides higher recognition accuracy for the wideband speech but also allows the same CD-DNN-HMM to recognize mixed-bandwidth speech. By exploiting mixed-bandwidth training data CD-DNN-HMM outperforms fMPE+BMMI trained GMM-HMM, which cannot benefit from using narrowband data, by 18.4%.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127735459","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: 140

Noisy channel adaptation in language identification 语言识别中的噪声信道适应

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424241

Sriram Ganapathy, M. Omar, Jason W. Pelecanos

{"title":"Noisy channel adaptation in language identification","authors":"Sriram Ganapathy, M. Omar, Jason W. Pelecanos","doi":"10.1109/SLT.2012.6424241","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424241","url":null,"abstract":"Language identification (LID) of speech data recorded over noisy communication channels is a challenging problem especially when the LID system is tested on speech data from an unseen communication channel (not seen in training). In this paper, we consider the scenario in which a small amount of adaptation data is available from a new communication channel. Various approaches are investigated for efficient utilization of the adaptation data in a supervised as well as unsupervised setting. In a supervised adaptation framework, we show that support vector machines (SVMs) with higher order polynomial kernels (HO-SVM) trained using lower dimensional representations of the the Gaussian mixture model supervectors (GSVs) provide significant performance improvements over the baseline SVM-GSV system. In these LID experiments, we obtain 30% reduction in error-rate with 6 hours of adaptation data for a new channel. For unsupervised adaptation, we develop an iterative procedure for re-labeling the development data using a co-training framework. In these experiments, we obtain considerable improvements(relative improvements of 13 %) over a self-training framework with the HO-SVM models.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127871347","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

Exemplar-based voice conversion in noisy environment 噪声环境下基于样本的语音转换

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424242

R. Takashima, T. Takiguchi, Y. Ariki

引用次数: 139

Evaluating the effect of normalizing informal text on TTS output 评估规范化非正式文本对TTS输出的影响

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424271

Deana Pennell, Yang Liu

引用次数: 1

Context dependent recurrent neural network language model 上下文相关的递归神经网络语言模型

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424228

Tomas Mikolov, G. Zweig

引用次数: 592

Statistical semantic interpretation modeling for spoken language understanding with enriched semantic features 面向语义特征丰富的口语理解的统计语义解释建模

2012 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2012-12-01 DOI: 10.1109/SLT.2012.6424225

Asli Celikyilmaz, Dilek Z. Hakkani-Tür, Gökhan Tür

{"title":"Statistical semantic interpretation modeling for spoken language understanding with enriched semantic features","authors":"Asli Celikyilmaz, Dilek Z. Hakkani-Tür, Gökhan Tür","doi":"10.1109/SLT.2012.6424225","DOIUrl":"https://doi.org/10.1109/SLT.2012.6424225","url":null,"abstract":"In natural language human-machine statistical dialog systems, semantic interpretation is a key task typically performed following semantic parsing, and aims to extract canonical meaning representations of semantic components. In the literature, usually manually built rules are used for this task, even for implicitly mentioned non-named semantic components (like genre of a movie or price range of a restaurant). In this study, we present statistical methods for modeling interpretation, which can also benefit from semantic features extracted from large in-domain knowledge sources. We extract features from user utterances using a semantic parser and additional semantic features from textual sources (online reviews, synopses, etc.) using a novel tree clustering approach, to represent unstructured information that correspond to implicit semantic components related to targeted slots in the user's utterances. We evaluate our models on a virtual personal assistance system and demonstrate that our interpreter is effective in that it does not only improve the utterance interpretation in spoken dialog systems (reducing the interpretation error rate by 36% relative compared to a language model baseline), but also unveils hidden semantic units that are otherwise nearly impossible to extract from purely manual lexical features that are typically used in utterance interpretation.","PeriodicalId":375378,"journal":{"name":"2012 IEEE Spoken Language Technology Workshop (SLT)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126812238","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