Modeling of linguistic and acoustic information from speech signal for multilingual spoken language identification system (SLID)

2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA) Pub Date : 2017-11-01 DOI:10.1109/ICSDA.2017.8384468

S. Bansal, S. Agrawal

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引用次数: 5

Abstract

Spoken language identification is the task of identifying a language from the given speech signal. Efforts to develop language identification systems for Indian languages have been very limited due to the problem of speaker availability and language legibility but the requirement of SLID is increasing for civil and defense applications day by day. The present paper reports a study to develop a multilingual identification system for two Indian languages i.e. Hindi and Manipuri by using PPRLM approach that requires phoneme based labeled speech corpus for each language. For each language, data set of 300 phonetically rich sentences spoken by 25 native speakers (15000 utterances) were recorded, analyzed and annotated phonemically to make trigram based phonotactic model. The features of the speech signal have been extracted using MFCCs and GMM was used as a classifier. Results show that accuracy increases with the increase of Gaussians and also with the training samples.

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多语言语音识别系统中语音信号的语言和声学信息建模

口语识别是从给定的语音信号中识别语言的任务。由于讲话者的可用性和语言的易读性问题，为印度语言开发语言识别系统的努力非常有限，但民用和国防应用对滑动识别系统的要求日益增加。本文报告了一项研究，利用PPRLM方法开发印地语和曼尼普尔语两种印度语言的多语言识别系统，该方法需要基于音素的标记语音语料库。对于每种语言，记录25名母语使用者的300个语音丰富的句子(15000个发音)数据集，对其进行语音分析和标注，构建基于三字母表的语音致音模型。使用mfcc提取语音信号的特征，并使用GMM作为分类器。结果表明，准确率随高斯分布的增加而增加，随训练样本的增加而增加。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA)

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