基于GMM-HMM的印地语语音识别声学模型比较

Chadalavada Sai Manasa, K. J. Priya, Deepa Gupta
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引用次数: 4

摘要

自动语音识别(ASR)在世界范围内得到了广泛的发展,它不仅可以作为人机界面的一部分,还可以用于各种各样的商业应用。在印度,使用自动语音识别的商业应用仍处于发展过程中。本文描述了使用CMU的PocketSphinx进行印地语语音识别的跨语言声学模型。准备了177个印地语单词的数据库,以及转录和词典。本文讨论了两种建立语音识别声学模型的方法。在第一种方法中,英语声学模型被跨语言适应于印地语。在第二种方法中,训练了不同的声学模型——连续的、半连续的和语音捆绑的模型。GMM-HMM用于声学建模和语言建模。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of acoustical models of GMM-HMM based for speech recognition in Hindi using PocketSphinx
Automatic Speech recognition (ASR) is widely gaining momentum worldwide, to be used as a part of Human Computer Interface and also in a wide variety of commercial applications. In Indian context, commercial applications using automatic speech recognition are still in the evolving process. This paper describes the acoustic models that have been cross language adapted for speech recognition in Hindi using CMU’s PocketSphinx. A database of 177 words in Hindi is prepared, along with transcription and dictionary. Two approaches for developing acoustical model for the speech recognition has been discussed in this paper. In the first approach, English acoustical model has been cross-language adapted to Hindi. In the second approach different acoustical models -continuous, semi continuous and phonetically tied models have been trained. GMM-HMM is used for acoustical modeling and language modeling.
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