A machine learning based speech enhancement method is proposed to improve the intelligibility of whispered speech. A binary mask estimated by a two-class support vector machine （SVM） classifier is used to synthesize...A machine learning based speech enhancement method is proposed to improve the intelligibility of whispered speech. A binary mask estimated by a two-class support vector machine （SVM） classifier is used to synthesize the enhanced whisper. A novel noise robust feature called Gammatone feature cosine coefficients （GFCCs） extracted by an auditory periphery model is derived and used for the binary mask estimation. The intelligibility performance of the proposed method is evaluated and compared with the traditional speech enhancement methods. Objective and subjective evaluation results indicate that the proposed method can effectively improve the intelligibility of whispered speech which is contaminated by noise. Compared with the power subtract algorithm and the log-MMSE algorithm, both of which do not improve the intelligibility in lower signal-to-noise ratio （SNR） environments, the proposed method has good performance in improving the intelligibility of noisy whisper. Additionally, the intelligibility of the enhanced whispered speech using the proposed method also outperforms that of the corresponding unprocessed noisy whispered speech.展开更多
At present, almost all the systems and products for speech recognition are working in quiet environment and their performances are degraded or even can′t work when they are operated in high noisy environment. In this...At present, almost all the systems and products for speech recognition are working in quiet environment and their performances are degraded or even can′t work when they are operated in high noisy environment. In this paper, after analyzing the features of speech and noise, a speech enhancement method for LPC autoregressive model for command words recognition used in noisy environment is proposed, and an experimental system is realized. In different background noisy environments, we conduct experiments about SNR, basic accuracy, noise resistant ability and system environment adaptability with different microphones. The experimental results show that the system has good recognition performance in high noisy environments. The system can resist many kinds of noises and meet the needs of application areas on the whole such as military, traffic, marketplace and factory etc.展开更多
基金The National Natural Science Foundation of China (No.61231002,61273266,51075068,60872073,60975017, 61003131)the Ph.D.Programs Foundation of the Ministry of Education of China(No.20110092130004)+1 种基金the Science Foundation for Young Talents in the Educational Committee of Anhui Province(No. 2010SQRL018)the 211 Project of Anhui University(No.2009QN027B)
文摘A machine learning based speech enhancement method is proposed to improve the intelligibility of whispered speech. A binary mask estimated by a two-class support vector machine （SVM） classifier is used to synthesize the enhanced whisper. A novel noise robust feature called Gammatone feature cosine coefficients （GFCCs） extracted by an auditory periphery model is derived and used for the binary mask estimation. The intelligibility performance of the proposed method is evaluated and compared with the traditional speech enhancement methods. Objective and subjective evaluation results indicate that the proposed method can effectively improve the intelligibility of whispered speech which is contaminated by noise. Compared with the power subtract algorithm and the log-MMSE algorithm, both of which do not improve the intelligibility in lower signal-to-noise ratio （SNR） environments, the proposed method has good performance in improving the intelligibility of noisy whisper. Additionally, the intelligibility of the enhanced whispered speech using the proposed method also outperforms that of the corresponding unprocessed noisy whispered speech.
文摘At present, almost all the systems and products for speech recognition are working in quiet environment and their performances are degraded or even can′t work when they are operated in high noisy environment. In this paper, after analyzing the features of speech and noise, a speech enhancement method for LPC autoregressive model for command words recognition used in noisy environment is proposed, and an experimental system is realized. In different background noisy environments, we conduct experiments about SNR, basic accuracy, noise resistant ability and system environment adaptability with different microphones. The experimental results show that the system has good recognition performance in high noisy environments. The system can resist many kinds of noises and meet the needs of application areas on the whole such as military, traffic, marketplace and factory etc.
