鲁棒自动语音识别系统:Hmm与稀疏

M. Mohammed, B. K. Edet, X. C. Carrol, K. A. Yasif, R. Rahamathulla, V. Supriya
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引用次数: 7

摘要

语音识别一直是研究人员和行业不断发展和具有挑战性的领域。在计算机领域,它被定义为计算机系统接受语音格式的口语的能力。Wav或raw并相应地执行任务。尽管商业应用广泛传播,但大多数研究工作都是用英语、阿拉伯语或普通话完成的,而且只有少数实验室知道其基础技术[1]。因此,这样一个系统的发展仍然处于原始阶段,以当地的印度语言。本文讨论了利用HMM工具包(HTK)开发马拉雅拉姆语数字识别系统的尝试。讨论了稀疏插值在识别算法中的应用,以提高识别算法的鲁棒性,而不是传统的隐马尔可夫模型技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust Automatic Speech Recognition System: Hmm Versus Sparse
Speech Recognition has been an ever growing and challenging area for the researchers as well as the industry. It is defined in the computer domain as the ability to ability of computer systems to accept spoken words in audio format - such as. wav or raw and perform tasks accordingly. Despite the wide diffusion of commercial applications most of the research works are done in either English, Arabic or Mandarin and the technology underlying is known to only a few laboratories[1]. Thus the development of such a system is still on the primitive stage towards the local Indian languages. This paper discusses regarding an attempt to develop a digit recognition system for Malayalam language using the HMM Toolkit (HTK). Application of Sparse Imputation to recognition algorithm is discussed so as to increase the robustness rather than the conventional Hidden Markov Model technique.
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