2018 IEEE Spoken Language Technology Workshop (SLT)最新文献_第3页

Analysing The Predictions Of a CNN-Based Replay Spoofing Detection System 基于cnn的重放欺骗检测系统预测分析

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639666

Bhusan Chettri, Saumitra Mishra, Bob L. Sturm, Emmanouil Benetos

引用次数: 29

End-To-End Named Entity And Semantic Concept Extraction From Speech 端到端命名实体与语音语义概念提取

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639513

Sahar Ghannay, Antoine Caubrière, Y. Estève, Nathalie Camelin, E. Simonnet, Antoine Laurent, E. Morin

{"title":"End-To-End Named Entity And Semantic Concept Extraction From Speech","authors":"Sahar Ghannay, Antoine Caubrière, Y. Estève, Nathalie Camelin, E. Simonnet, Antoine Laurent, E. Morin","doi":"10.1109/SLT.2018.8639513","DOIUrl":"https://doi.org/10.1109/SLT.2018.8639513","url":null,"abstract":"Named entity recognition (NER) is among SLU tasks that usually extract semantic information from textual documents. Until now, NER from speech is made through a pipeline process that consists in processing first an automatic speech recognition (ASR) on the audio and then processing a NER on the ASR outputs. Such approach has some disadvantages (error propagation, metric to tune ASR systems sub-optimal in regards to the final task, reduced space search at the ASR output level,...) and it is known that more integrated approaches outperform sequential ones, when they can be applied. In this paper, we explore an end-to-end approach that directly extracts named entities from speech, though a unique neural architecture. On a such way, a joint optimization is possible for both ASR and NER. Experiments are carried on French data easily accessible, composed of data distributed in several evaluation campaigns. The results are promising since this end-to-end approach provides similar results (F-measure= 0.66 on test data) than a classical pipeline approach to detect named entity categories (F-measure=0.64). Last, we also explore this approach applied to semantic concept extraction, through a slot filling task known as a spoken language understanding problem, and also observe an improvement in comparison to a pipeline approach.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"138 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117299283","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: 75

An Exploration of Directly Using Word as ACOUSTIC Modeling Unit for Speech Recognition 直接使用词作为语音识别声学建模单元的探索

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639623

Chunlei Zhang, Chengzhu Yu, Chao Weng, Jia Cui, Dong Yu

引用次数: 6

Training Speaker Recognition Models with Recording-Level Labels 用录音级标签训练说话人识别模型

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/slt.2018.8639601

Tanel Alumae

引用次数: 3

Investigating Deep Neural Networks for Speaker Diarization in the DIHARD Challenge 在DIHARD挑战中研究深度神经网络对说话人分化的影响

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639630

Ivan Himawan, M. Rahman, S. Sridharan, C. Fookes, A. Kanagasundaram

引用次数: 5

Sequence Teacher-Student Training of Acoustic Models for Automatic Free Speaking Language Assessment 自由口语自动评估声学模型的顺序师生训练

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639557

Yu Wang, J. H. M. Wong, M. Gales, K. Knill, A. Ragni

引用次数: 10

Investigating the Downstream Impact of Grapheme-Based Acoustic Modeling on Spoken Utterance Classification 基于石墨烯的声学建模对语音分类的影响研究

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639549

Ryan Price, Bhargav Srinivas Ch, Surbhi Singhal, S. Bangalore

{"title":"Investigating the Downstream Impact of Grapheme-Based Acoustic Modeling on Spoken Utterance Classification","authors":"Ryan Price, Bhargav Srinivas Ch, Surbhi Singhal, S. Bangalore","doi":"10.1109/SLT.2018.8639549","DOIUrl":"https://doi.org/10.1109/SLT.2018.8639549","url":null,"abstract":"Automatic speech recognition (ASR) and natural language understanding are critical components of spoken language understanding (SLU) systems. One obstacle to providing services with SLU systems in multiple languages is the cost associated with acquiring all of the language-specific resources required for ASR in each language. Modeling graphemes eliminates the need to obtain a pronunciation dictionary which maps from speech sounds to words and is one way to reduce ASR resource dependencies when rapidly developing ASR in new languages. However, little is known about the downstream impact on SLU task performance when selecting graphemes as the acoustic modeling unit. This work investigates acoustic modeling for the ASR component of an SLU system using grapheme-based approaches together with convolutional and recurrent neural network architectures. We evaluate both ASR word accuracy and spoken utterance classification (SUC) accuracy for English, Italian and Spanish language tasks and find that it is possible to achieve SUC accuracy that is comparable to conventional phoneme-based systems which leverage a pronunciation dictionary.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126624652","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

Intelligence Is Asking The Right Question: A Study On Japanese Question Generation 智力就是问对问题:日语问题生成研究

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639526

Lasguido Nio, Koji Murakami

引用次数: 4

Optimizing the Quality of Synthetically Generated Pseudowords for the Task of Minimal-Pair Distinction 优化最小对区分任务中合成假词的质量

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639037

Heiko Holz, Maria Chinkina, Laura Vetter

引用次数: 0

Exploring End-To-End Attention-Based Neural Networks For Native Language Identification 探索端到端基于注意力的神经网络用于母语识别

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639689

Rutuja Ubale, Yao Qian, Keelan Evanini

引用次数: 13