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首页> 外文期刊>Indian Journal of Science and Technology >Speech Recognition using Hidden Markov Models in Embedded Platform
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Speech Recognition using Hidden Markov Models in Embedded Platform

机译:嵌入式平台中使用隐马尔可夫模型的语音识别

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摘要

Speech recognition used widely in environment of mobile application. The Study of speech recognition is one of the main topics of journal in artificial intelligence which doesn’t take out meaningful result. The reason of such result is to relate with difficulty of feature extraction. Liner Predictive Coding, Hidden Markov Model, Artificial Neural Network are known to be effective in the same way as for the voice signal processing. Mel frequency cepstral coefficients are the most popular method for extracting speech features from the speech recognition field. This paper proposes a method for recognizing speech using Mel-frequency information. In this paper we propose Automatic Speech Recognition (ASR) technique using Mel-Frequency Cepstral Coefficient extraction and Hidden Markov Model in mobile environment. Our method is used to speech enhancement technique. We’ll try to implement ASR system in mobile environment using embedded platform.
机译:语音识别广泛应用于移动应用环境中。语音识别研究是人工智能杂志的主要主题之一,并未取得有意义的成果。这种结果的原因与特征提取的困难有关。众所周知,线性预测编码,隐马尔可夫模型,人工神经网络与语音信号处理一样有效。梅尔频率倒谱系数是从语音识别领域提取语音特征的最流行方法。提出了一种基于梅尔频率信息的语音识别方法。在本文中,我们提出了在移动环境中使用Mel频率倒谱系数提取和隐马尔可夫模型的自动语音识别(ASR)技术。我们的方法用于语音增强技术。我们将尝试使用嵌入式平台在移动环境中实施ASR系统。

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