Workshop on Spoken Language Technologies for Under-resourced Languages最新文献_第3页

Advances in Low Resource ASR: A Deep Learning Perspective 基于深度学习的低资源ASR研究进展

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-4

Hardik B. Sailor, Ankur T. Patil, H. Patil

引用次数: 5

Mining Training Data for Language Modeling Across the World's Languages 跨世界语言的语言建模训练数据挖掘

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-13

Manasa Prasad, Theresa Breiner, D. Esch

引用次数: 12

Optimizing DPGMM Clustering in Zero Resource Setting Based on Functional Load 基于功能负载的零资源环境下DPGMM聚类优化

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-1

Bin Wu, S. Sakti, Jinsong Zhang, Satoshi Nakamura

引用次数: 10

Development of Assamese Continuous Speech Recognition System 阿萨姆语连续语音识别系统的开发

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-46

Tanmay Bhowmik, S. Mandal

引用次数: 0

Analysis and Comparison of Features for Text-Independent Bengali Speaker Recognition 不依赖文本的孟加拉语说话人识别特征分析与比较

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-57

S. Das, P. Das

引用次数: 0

Improved Language Identification Using Stacked SDC Features and Residual Neural Network 基于堆叠SDC特征和残差神经网络的改进语言识别

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-44

R. Vuddagiri, Hari Krishna Vydana, A. Vuppala

引用次数: 9

Signal Processing Cues to Improve Automatic Speech Recognition for Low Resource Indian Languages 信号处理线索改善低资源印度语言的自动语音识别

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-6

Arun Baby, S. KarthikPandiaD., H. Murthy

{"title":"Signal Processing Cues to Improve Automatic Speech Recognition for Low Resource Indian Languages","authors":"Arun Baby, S. KarthikPandiaD., H. Murthy","doi":"10.21437/SLTU.2018-6","DOIUrl":"https://doi.org/10.21437/SLTU.2018-6","url":null,"abstract":"Building accurate acoustic models for low resource languages is the focus of this paper. Acoustic models are likely to be accurate provided the phone boundaries are determined accurately. Conventional ﬂat-start based Viterbi phone alignment (where only utterance level transcriptions are available) results in poor phone boundaries as the boundaries are not explicitly modeled in any statistical machine learning system. The focus of the effort in this paper is to explicitly model phrase boundaries using acoustic cues obtained using signal processing. A phrase is made up of a sequence of words, where each word is made up of a sequence of syllables. Syllable boundaries are detected using signal processing. The waveform corresponding to an utterance is spliced at phrase boundaries when it matches a syllable boundary. Gaussian mixture model - hidden Markov model (GMM-HMM) training is performed phrase by phrase, rather than utterance by utterance. Training using these short phrases yields better acoustic models. This alignment is then fed to a DNN to enable better discrimination between phones. During the training process, the syllable boundaries (obtained using signal processing) are restored in every iteration. A rela-tive improvement is observed in WER over the baseline Indian languages, namely, Gujarati, Tamil, and Telugu.","PeriodicalId":190269,"journal":{"name":"Workshop on Spoken Language Technologies for Under-resourced Languages","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128106054","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

JAMLIT: A Corpus of Jamaican Standard English for Automatic Speech Recognition of Children's Speech JAMLIT:用于儿童语音自动识别的牙买加标准英语语料库

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-51

Stefan Watson, André Coy

引用次数: 0

Empirical Study of Speech Synthesis Markup Language and Its Implementation for Punjabi Language 旁遮普语语音合成标记语言的实证研究及实现

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-22

Atul Kumar, S. Agrawal

引用次数: 0

Implementation of Concatenation Technique for Low Resource Text-To-Speech System Based on Marathi Talking Calculator 基于马拉地语语音计算器的低资源文本转语音系统级联技术实现

Workshop on Spoken Language Technologies for Under-resourced Languages Pub Date : 2018-08-29 DOI: 10.21437/SLTU.2018-16

Monica R. Mundada, Sangramsing Kayte, P. Das

{"title":"Implementation of Concatenation Technique for Low Resource Text-To-Speech System Based on Marathi Talking Calculator","authors":"Monica R. Mundada, Sangramsing Kayte, P. Das","doi":"10.21437/SLTU.2018-16","DOIUrl":"https://doi.org/10.21437/SLTU.2018-16","url":null,"abstract":"The indulgent acquaintance of mathematical basic concepts creates the pavement for numerous opportunities in life for every individual, including visually impaired people. The use of assertive technology for the disabled section of the society makes them more independent and avoid barriers in the field of education and employment. This research is focused to design an Android-based application i.e. talking Calculator for low resource based Marathi native language. The novelty of this work is to develop both, the application and the Marathi number corpus. Marathi is an Indo-Aryan language spoken by approximately 6.99 million speakers in India, which is the third widely spoken language after Bengali and Telugu but as they lack in linguistic resources, e.g. grammars, POS taggers, corpora, it falls into the category of low resource languages. The front end part of the application depicts the screen of a basic calculator with numerals displayed in Marathi. During runtime, each number is spoken as the specific key is pressed. It also speaks out the operation which is intended to be performed. The concatenation synthesis technique is applied to speak out the value of decimal places in the output number. The result is spoken out with proper place value of a digit in Marathi. The performance of the system is measured to the accuracy rate of 95.5%. The average run time complexity of the application is also calculated which is noted down to 2.64 sec. The feedback and review of the application is also taken from real end-user i.e. blind people.","PeriodicalId":190269,"journal":{"name":"Workshop on Spoken Language Technologies for Under-resourced Languages","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121508920","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: 0