为了提高说话人识别的准确率,可以同时采用多个特征参数,针对综合特征参数中各维分量对识别结果的影响可能不一样,同等对待并不一定是最优的方案这个问题,提出基于Fisher准则的梅尔频率倒谱系数(MFCC)、线性预测梅尔倒谱系数(LPMFCC)、T...为了提高说话人识别的准确率,可以同时采用多个特征参数,针对综合特征参数中各维分量对识别结果的影响可能不一样,同等对待并不一定是最优的方案这个问题,提出基于Fisher准则的梅尔频率倒谱系数(MFCC)、线性预测梅尔倒谱系数(LPMFCC)、Teager能量算子倒谱参数(TEOCC)相混合的特征参数提取方法。首先,提取语音信号的MFCC、LPMFCC和TEOCC三种参数;然后,计算MFCC和LPMFCC参数中各维分量的Fisher比,分别选出六个Fisher比高的分量与TEOCC参数组合成混合特征参数;最后,采用TIMIT语音库和NOISEX-92噪声库进行说话人识别实验。仿真实验表明,所提方法与MFCC、LPMFCC、MFCC+LPMFCC、基于Fisher比的梅尔倒谱系数混合特征提取方法以及基于主成分分析(PCA)的特征抽取方法相比,在采用高斯混合模型(GMM)和BP神经网络的平均识别率在纯净语音环境下分别提高了21.65个百分点、18.39个百分点、15.61个百分点、15.01个百分点与22.70个百分点;在30 d B噪声环境下,则分别提升了15.15个百分点、10.81个百分点、8.69个百分点、7.64个百分点与17.76个百分点。实验结果表明,该混合特征参数能够有效提高说话人识别率,且具有更好的鲁棒性。展开更多
One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimen...One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimensional feature extraction subsystem shows low recognition rate for identifying an utterance speech signal under harsh noise conditions, we have developed a speaker identification system based on two-dimensional Bispectrum data that was theoretically more robust to the addition of Gaussian noise. As the processing sequence of ID-MFCC method could not be directly used for processing the two-dimensional Bispectrum data, in this paper we proposed a 2D-MFCC method as an extension of the 1D-MFCC method and the optimization of the 2D filter design using Genetic Algorithms. By using the 2D-MFCC method with the Bispectrum analysis method as the feature extraction technique, we then used Hidden Markov Model as the pattern classifier. In this paper, we have experimentally shows our developed methods for identifying an utterance speech signal buried with various levels of noise. Experimental result shows that the 2D-MFCC method without GA optimization has a comparable high recognition rate with that of 1D-MFCC method for utterance signal without noise addition. However, when the utterance signal is buried with Gaussian noises, the developed 2D-MFCC shows higher recognition capability, especially, when the 2D-MFCC optimized by Genetics Algorithms is utilized.展开更多
In speech recognition systems, the physiological characteristics of the speech production model cause the voiced sections of the speech signal to have an attenuation of approximately 20 dB per decade. Many speech rec...In speech recognition systems, the physiological characteristics of the speech production model cause the voiced sections of the speech signal to have an attenuation of approximately 20 dB per decade. Many speech recognition algorithms have been developed to solve this problem by filtering the input signal with a single-zero high pass filter. Unfortunately, this technique increases the noise energy at high frequencies above 4 kHz, which in some cases degrades the recognition accuracy. This paper solves the problem using a pre-emphasis filter in the front end of the recognizer. The aim is to develop a modified parameterization approach taking into account the whole energy zone in the spectrum to improve the performance of the existing baseline recognition system in the acoustic phase. The results show that a large vocabulary speaker-independent continuous speech recognition system using this approach has a greatly improved recognition rate.展开更多
文摘为了提高说话人识别的准确率,可以同时采用多个特征参数,针对综合特征参数中各维分量对识别结果的影响可能不一样,同等对待并不一定是最优的方案这个问题,提出基于Fisher准则的梅尔频率倒谱系数(MFCC)、线性预测梅尔倒谱系数(LPMFCC)、Teager能量算子倒谱参数(TEOCC)相混合的特征参数提取方法。首先,提取语音信号的MFCC、LPMFCC和TEOCC三种参数;然后,计算MFCC和LPMFCC参数中各维分量的Fisher比,分别选出六个Fisher比高的分量与TEOCC参数组合成混合特征参数;最后,采用TIMIT语音库和NOISEX-92噪声库进行说话人识别实验。仿真实验表明,所提方法与MFCC、LPMFCC、MFCC+LPMFCC、基于Fisher比的梅尔倒谱系数混合特征提取方法以及基于主成分分析(PCA)的特征抽取方法相比,在采用高斯混合模型(GMM)和BP神经网络的平均识别率在纯净语音环境下分别提高了21.65个百分点、18.39个百分点、15.61个百分点、15.01个百分点与22.70个百分点;在30 d B噪声环境下,则分别提升了15.15个百分点、10.81个百分点、8.69个百分点、7.64个百分点与17.76个百分点。实验结果表明,该混合特征参数能够有效提高说话人识别率,且具有更好的鲁棒性。
文摘One-dimensional Mel-Frequency Cepstrum Coefficients (1D-MFCC) in conjunction with a power spectrum analysis method is usually used as a feature extraction in a speaker identification system. However, as this one dimensional feature extraction subsystem shows low recognition rate for identifying an utterance speech signal under harsh noise conditions, we have developed a speaker identification system based on two-dimensional Bispectrum data that was theoretically more robust to the addition of Gaussian noise. As the processing sequence of ID-MFCC method could not be directly used for processing the two-dimensional Bispectrum data, in this paper we proposed a 2D-MFCC method as an extension of the 1D-MFCC method and the optimization of the 2D filter design using Genetic Algorithms. By using the 2D-MFCC method with the Bispectrum analysis method as the feature extraction technique, we then used Hidden Markov Model as the pattern classifier. In this paper, we have experimentally shows our developed methods for identifying an utterance speech signal buried with various levels of noise. Experimental result shows that the 2D-MFCC method without GA optimization has a comparable high recognition rate with that of 1D-MFCC method for utterance signal without noise addition. However, when the utterance signal is buried with Gaussian noises, the developed 2D-MFCC shows higher recognition capability, especially, when the 2D-MFCC optimized by Genetics Algorithms is utilized.
基金Supported by the National High- TechnologyDevelopm ent Program of China(No.2 0 0 1AA1140 71)
文摘In speech recognition systems, the physiological characteristics of the speech production model cause the voiced sections of the speech signal to have an attenuation of approximately 20 dB per decade. Many speech recognition algorithms have been developed to solve this problem by filtering the input signal with a single-zero high pass filter. Unfortunately, this technique increases the noise energy at high frequencies above 4 kHz, which in some cases degrades the recognition accuracy. This paper solves the problem using a pre-emphasis filter in the front end of the recognizer. The aim is to develop a modified parameterization approach taking into account the whole energy zone in the spectrum to improve the performance of the existing baseline recognition system in the acoustic phase. The results show that a large vocabulary speaker-independent continuous speech recognition system using this approach has a greatly improved recognition rate.
