基于自适应心理声学模型的智能语音识别系统
发布时间：2018-09-19 14:46
【摘要】：针对包含环境噪声和信道失真等噪声的语音处理问题,提出了一种基于自适应心理声学模型的智能语音识别系统,并建立了听觉模型.该模型将心理声学和耳声发射(OAE)合并到了自动语音识别(ASR)系统中,利用AURORA2数据库分别在清洁训练条件和多训练条件下进行试验.结果表明,所提出的特征提取方法可以显著提高词识别率,优于梅尔频率倒谱系数(MFCC)、前向掩蔽(FM)、侧向抑制(LI)和倒谱平均值及方差归一化(CMVN)算法,能够有效地提高智能语音识别系统的性能.
[Abstract]:In this paper, an intelligent speech recognition system based on adaptive psychoacoustic model is proposed to deal with speech processing problems including ambient noise and channel distortion, and an auditory model is established. The model combines psychoacoustics and otoacoustic emission (OAE) (OAE) into automatic speech recognition (ASR) system. The AURORA2 database is used to test under the condition of clean training and multiple training respectively. The results show that the proposed feature extraction method can significantly improve the word recognition rate, and is better than the Mel frequency cepstrum coefficient (MFCC), forward masking (FM), lateral suppression (LI), cepstrum mean and variance normalized (CMVN) algorithm. It can effectively improve the performance of the intelligent speech recognition system.
【作者单位】： 南昌大学科学技术学院;
【基金】：江西省教育厅科学技术研究项目(GJJ151504,GJJ151505) 江西省教育改革课题资助项目(JXJG-14-28-3,JXJG-14-28-1,JXJG-14-28-6,JXJG-14-28-8)
【分类号】：TN912.34
[Abstract]:In this paper, an intelligent speech recognition system based on adaptive psychoacoustic model is proposed to deal with speech processing problems including ambient noise and channel distortion, and an auditory model is established. The model combines psychoacoustics and otoacoustic emission (OAE) (OAE) into automatic speech recognition (ASR) system. The AURORA2 database is used to test under the condition of clean training and multiple training respectively. The results show that the proposed feature extraction method can significantly improve the word recognition rate, and is better than the Mel frequency cepstrum coefficient (MFCC), forward masking (FM), lateral suppression (LI), cepstrum mean and variance normalized (CMVN) algorithm. It can effectively improve the performance of the intelligent speech recognition system.
【作者单位】： 南昌大学科学技术学院;
【基金】：江西省教育厅科学技术研究项目(GJJ151504,GJJ151505) 江西省教育改革课题资助项目(JXJG-14-28-3,JXJG-14-28-1,JXJG-14-28-6,JXJG-14-28-8)
【分类号】：TN912.34
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