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首页> 外文期刊>Journal of computer science engineering and information technology research >A STATISTICAL ANALYSIS ON THE IMPACT OF SPEECH ENHANCEMENT TECHNIQUES ON THE FEATURE VECTORS OF NOISY SPEECH SIGNALS FOR SPEECH RECOGNITION
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A STATISTICAL ANALYSIS ON THE IMPACT OF SPEECH ENHANCEMENT TECHNIQUES ON THE FEATURE VECTORS OF NOISY SPEECH SIGNALS FOR SPEECH RECOGNITION

机译：语音增强技术对语音识别中嘈杂语音信号特征向量影响的统计分析

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摘要

Noise is one of the major challenges in the development of robust automatic speech recognition (ASR) System. There are several speech enhancement techniques available to reduce the effect of noise from speech signals. In this paper, a statistical analysis is presented on the impact of speech enhancement techniques on the feature vectors of noisy speech signals by estimating Bhattacharya distances (BD) from the feature vectors of approximately noise free training speech signals to the feature vectors of noisy testing speech signals. Here Sub-band Spectral Subtraction (SSS) and Frame Selection (FS) have been used as speech enhancement techniques at signal level and Cepstral Mean Normalization (CMN) has been used as feature normalization technique at feature level. In this research work, combination of Mel-Frequency Cepstral Coefficients (MFCC), Log energies, first time derivatives and second time derivatives of MFCCs and Log energies has been used as speech feature vectors. Speech recognition experiments have been also performed to recognize English vowel phonemes in this research work where the recognizer has been developed using pattern recognition approach applying Hidden Markov Model (HMM).
机译：噪声是开发强大的自动语音识别（ASR）系统的主要挑战之一。有几种语音增强技术可用于减少语音信号的噪声影响。在本文中，通过估计从近似无噪声的训练语音信号的特征向量到有噪声的测试语音的特征向量的Bhattacharya距离（BD），对语音增强技术对嘈杂的语音信号的特征向量的影响进行了统计分析。信号。这里，子带频谱减法（SSS）和帧选择（FS）已被用作信号级别的语音增强技术，而倒谱均值归一化（CMN）已被用作特征级别的特征归一技术。在这项研究工作中，已将梅尔频率倒谱系数（MFCC），对数能量，MFCC和对数能量的一阶导数和二阶导数结合起来用作语音特征向量。在这项研究工作中，还进行了语音识别实验来识别英语元音音素，其中识别器是使用隐马尔可夫模型（HMM）使用模式识别方法开发的。

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