基于人工神经网络的语音识别

C. Vivek, M. Indu, N. Nandhini
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摘要

言语是人类通过语言进行的口头交流。同样,语音识别是将语音转换为文本的过程。本文对人工神经网络在语音识别中的应用进行了研究。隐马尔可夫模型(HMM)是一种用于语音识别的传统统计技术。在语音检测软件中,频繁使用Mel倒谱系数(MFCCs)。随着不同方法的发展,我们讨论了用于识别语音模式的特征以及在高效类型的人工神经网络(ANN)中语音识别的实现。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Speech Recognition Using Artificial Neural Network
Speech is a verbal communication used by humans through language. Likewise speech recognition is a process of converting speech to text. This paper provides a study of use of artificial neural networks(ANN) in speech recognition. Hidden Markov models (HMM) is a traditional statistical techniques for performing speech recognition. In speech detection software, Mel frequency cepstral coefficients (MFCCs) are frequently used. With different approaches evolving, we deal with the features used to recognize the speech pattern and implementation of speech recognition in the efficient types of artificial neural network (ANN).
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