2018 IEEE Spoken Language Technology Workshop (SLT)最新文献_第6页

Improving LF-MMI Using Unconstrained Supervisions for ASR 利用无约束监督改进ASR的LF-MMI

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639684

Hossein Hadian, Daniel Povey, H. Sameti, J. Trmal, S. Khudanpur

引用次数: 11

Neural TTS Voice Conversion 神经TTS语音转换

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639550

Zvi Kons, Slava Shechtman, A. Sorin, R. Hoory, Carmel Rabinovitz, E. Morais

引用次数: 10

WaveNet-Based Zero-Delay Lossless Speech Coding 基于波形的零延迟无损语音编码

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639598

Takenori Yoshimura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, K. Tokuda

引用次数: 5

Querying Depression Vlogs 查询抑郁症视频日志

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639555

M. J. Correia, B. Raj, I. Trancoso

{"title":"Querying Depression Vlogs","authors":"M. J. Correia, B. Raj, I. Trancoso","doi":"10.1109/SLT.2018.8639555","DOIUrl":"https://doi.org/10.1109/SLT.2018.8639555","url":null,"abstract":"Speech based diagnosis-aid tools for depression typically depend on few and small datasets, that are expensive to collect. The limited availability of training data poses a limitation to the quality that these systems can achieve. An unexplored alternative for large scale source of data are vlogs collected from online multimedia repositories. Along with the automation of the mining process, it is necessary to automate the labeling process too.In this work, we propose a framework to automatically label a corpus of in-the-wild vlogs of possibly depressed subjects, and we estimate the quality of the predicted labels, without ever having access to a ground truth for the majority of the corpus. The framework uses a small subset to train a model and estimate the labels for the remainder of the corpus. Then, using the predicted labels, we train a noisy model and attempt to reconstruct the labels of the original labeled subset. We hypothesize that the quality of the estimated labels for the unlabelled subset of the corpus is correlated to the quality of the label reconstruction of the labeled subset.The results of the bi-modal experiment using in-the-wild data are compared to the ones obtained using controlled data.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"5 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":"129984670","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

SLT 2018 Index

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/slt.2018.8639567

引用次数: 0

DNN-Based Scoring of Language Learners’ Proficiency Using Learners’ Shadowings and Native Listeners’ Responsive Shadowings 基于dnn的学习者阴影和母语听者反应性阴影的语言水平评分

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639645

Suguru Kabashima, Y. Inoue, D. Saito, N. Minematsu

{"title":"DNN-Based Scoring of Language Learners’ Proficiency Using Learners’ Shadowings and Native Listeners’ Responsive Shadowings","authors":"Suguru Kabashima, Y. Inoue, D. Saito, N. Minematsu","doi":"10.1109/SLT.2018.8639645","DOIUrl":"https://doi.org/10.1109/SLT.2018.8639645","url":null,"abstract":"This paper investigates DNN-based scoring techniques when they are applied to two tasks related to foreign language education. One is a conventional task, which attempts to predict a language learner’s overall proficiency of oral communication. For this aim, learners’ shadowing utterances are assessed automatically. The other is a very new and novel task, which attempts to predict intelligibility or comprehensibility of a learner’s pronunciation. In this task, native listeners’ responsive shadowings are assessed. For both the tasks, similar technical frameworks are tested, where DNN-based phoneme posteriors, posteriogram-based DTW scores, ASR-based accuracies, shadowing latencies, etc are used to train regression models, which aim to predict manually rated scores. Experiments show that, in both the tasks, the correlation between the DNN-based predicted scores and the averaged human scores is higher than or at least comparable to the averaged correlation between the scores of human raters. This fact clearly indicates that our proposed automatic rating module can be introduced to language education as another human rater.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"14 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":"127620870","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

Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR 基于外部语言模型的声-词ASR中OOV检测和分辨率的改进

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639563

H. Inaguma, M. Mimura, S. Sakai, Tatsuya Kawahara

{"title":"Improving OOV Detection and Resolution with External Language Models in Acoustic-to-Word ASR","authors":"H. Inaguma, M. Mimura, S. Sakai, Tatsuya Kawahara","doi":"10.1109/SLT.2018.8639563","DOIUrl":"https://doi.org/10.1109/SLT.2018.8639563","url":null,"abstract":"Acoustic-to-word (A2W) end-to-end automatic speech recognition (ASR) systems have attracted attention because of an extremely simplified architecture and fast decoding. To alleviate data sparseness issues due to infrequent words, the combination with an acoustic-to-character (A2C) model is investigated. Moreover, the A2C model can be used to recover-of-vocabulary (OOV) words that are not covered by the A2W model, but this requires accurate detection of OOV words. A2W models learn contexts with both acoustic and transcripts; therefore they tend to falsely recognize OOV words as words in the vocabulary. In this paper, we tackle this problem by using external language models (LM), which are trained only with transcriptions and have better linguistic information to detect OOV words. The A2C model is used to resolve these OOV words. Experimental evaluations show that external LMs have the effects of not only reducing errors but also increasing the number of detected OOV words, and the proposed method significantly improves performances in English conversational and Japanese lecture corpora, especially for-of-domain scenario. We also investigate the impact of the vocabulary size of A2W models and the data size for training LMs. Moreover, our approach can reduce the vocabulary size several times with marginal performance degradation.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"66 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":"126296033","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: 4

Multi-Band Processing With Gabor Filters and Time Delay Neural Nets for Noise Robust Speech Recognition 基于Gabor滤波器和时滞神经网络的多频带噪声鲁棒语音识别

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639631

György Kovács, L. Tóth, G. Gosztolya

引用次数: 2

Extension of Conventional Co-Training Learning Strategies to Three-View and Committee-Based Learning Strategies for Effective Automatic Sentence Segmentation 将传统的共同训练学习策略扩展到基于三视图和委员会的有效自动分句学习策略

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639533

Dogan Dalva, Ümit Güz, Hakan Gürkan

{"title":"Extension of Conventional Co-Training Learning Strategies to Three-View and Committee-Based Learning Strategies for Effective Automatic Sentence Segmentation","authors":"Dogan Dalva, Ümit Güz, Hakan Gürkan","doi":"10.1109/SLT.2018.8639533","DOIUrl":"https://doi.org/10.1109/SLT.2018.8639533","url":null,"abstract":"The objective of this work is to develop effective multiview semi-supervised machine learning strategies for sentence boundary classification problem when only small sets of sentence boundary labeled data are available. We propose three-view and committee-based learning strategies incorporating with co-training algorithms with agreement, disagreement, and self-combined learning strategies using prosodic, lexical and morphological information. We compare experimental results of proposed three-view and committee-based learning strategies to other semi-supervised learning strategies in the literature namely, self-training and co-training with agreement, disagreement, and self-combined strategies. The experiment results show that sentence segmentation performance can be highly improved using multi-view learning strategies that we propose since data sets can be represented by three redundantly sufficient and disjoint feature sets. We show that the proposed strategies substantially improve the average performance when only a small set of manually labeled data is available for Turkish and English spoken languages, respectively.","PeriodicalId":377307,"journal":{"name":"2018 IEEE Spoken Language Technology Workshop (SLT)","volume":"28 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":"128147901","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 Prompt-Aware Neural Network Approach to Content-Based Scoring of Non-Native Spontaneous Speech 基于内容的非母语自发语音评分的提示感知神经网络方法

2018 IEEE Spoken Language Technology Workshop (SLT) Pub Date : 2018-12-01 DOI: 10.1109/SLT.2018.8639697

Yao Qian, Rutuja Ubale, Matthew David Mulholland, Keelan Evanini, Xinhao Wang

引用次数: 12