{"title":"Development of IIITH Hindi-English Code Mixed Speech Database","authors":"B. Rambabu, S. Gangashetty","doi":"10.21437/SLTU.2018-23","DOIUrl":null,"url":null,"abstract":"This paper presents the design and development of IIITH Hindi-English code mixed (IIITH-HE-CM) text and corresponding speech corpus. The corpus is collected from several Hindi native speakers from different geographical parts of India. The IIITH-HE-CM corpus has phonetically balanced code mixed sentences with all the phoneme coverage of Hindi and English languages. We used triphone frequency of word internal triphone sequence, consists the language specific information, which helps in code mixed speech recognition and language modelling. The code mixed sentences are written in Devanagari script. Since computers can recognize Roman symbols, we used Indian Language Speech Sound Label (ILSL) transcription. An acoustic model is built for Hindi-English mixed language in-stead of language-dependent models. A large vocabulary code-mixing speech recognition system is developed based on a deep neural network (DNN) architecture. The proposed code-mixed speech recognition system attains low word error rate (WER) compared to conventional system.","PeriodicalId":190269,"journal":{"name":"Workshop on Spoken Language Technologies for Under-resourced Languages","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Workshop on Spoken Language Technologies for Under-resourced Languages","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21437/SLTU.2018-23","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents the design and development of IIITH Hindi-English code mixed (IIITH-HE-CM) text and corresponding speech corpus. The corpus is collected from several Hindi native speakers from different geographical parts of India. The IIITH-HE-CM corpus has phonetically balanced code mixed sentences with all the phoneme coverage of Hindi and English languages. We used triphone frequency of word internal triphone sequence, consists the language specific information, which helps in code mixed speech recognition and language modelling. The code mixed sentences are written in Devanagari script. Since computers can recognize Roman symbols, we used Indian Language Speech Sound Label (ILSL) transcription. An acoustic model is built for Hindi-English mixed language in-stead of language-dependent models. A large vocabulary code-mixing speech recognition system is developed based on a deep neural network (DNN) architecture. The proposed code-mixed speech recognition system attains low word error rate (WER) compared to conventional system.
本文介绍了IIITH印英混合码(IIITH- he - cm)文本和相应语音语料库的设计与开发。该语料库收集了来自印度不同地理区域的几个印地语母语人士。IIITH-HE-CM语料库具有语音平衡的代码混合句子,具有印地语和英语语言的所有音素覆盖。我们使用三音频率的词内部三音序列,组成语言的特定信息,这有助于代码混合语音识别和语言建模。代码混合语句是用梵文书写的。由于计算机可以识别罗马符号,我们使用了印度语言语音标签(ILSL)转录。在此基础上,建立了一种针对印英混合语言的声学模型,而不是基于语言的模型。提出了一种基于深度神经网络(DNN)的大词汇量混码语音识别系统。与传统语音识别系统相比,该系统具有较低的单词错误率。