一种基于HMM的噪声鲁棒自动语音识别新模型

M. S. Rafieee, A. Khazaei
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引用次数: 5

摘要

本文提出了一种基于并行分支隐马尔可夫模型(HMM)结构的噪声鲁棒自动语音识别模型,为鲁棒语音识别提供了一种新的途径。自动语音识别应用程序,如语音命令和控制,音频索引,语音到语音翻译,通常不能很好地在嘈杂的环境中工作。在本文中,我们提出了一个新的模型的特点,通过探索振动电图和肌电图ASR方法和其他一些有效的方法来达到最好的结果。利用该模型,我们得到了单词错误率、带截止频率的可用带宽、单词识别率等参数。本文包括计算量较少的高级前端处理和大词汇量肌电语音的统计建模。为此,研究了自适应共振系统的参数估计,如mel -频率倒谱系数(MFCC),以建立统计优化模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel model characteristics for noise-robust Automatic Speech Recognition based on HMM
This paper proposes a new model for a noise-robust Automatic Speech Recognition (ASR) based on parallel branch Hidden Markov Model (HMM) structure with a novel approach for robust speech recognition. Automatic Speech Recognition applications such as voice command and control, audio indexing, speech-to-speech translation, do not usually work well in noisy environments. In this paper, we present the characteristics of a novel model by exploring vibrocervigraphic and electromyographic ASR methods and some other effective approaches to achieve the best results. By employing the proposed model, we obtain the word error rate, available bandwidths with cutoff frequencies, word recognition rate, etc. This paper includes advanced front-end processing with less computational requirements and a statistical modeling for large-vocabulary myoelectric speech. Therefore parameters estimation of ASR system like Mel-Frequency Cepstral Coefficients (MFCC) are investigated to create the statistically optimized model.
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