In this paper,two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) es-timator under the assumption that the DCT...In this paper,two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) es-timator under the assumption that the DCT coefficients of clean speech are modeled by a Laplacian or a Gamma distribution and the DCT coefficients of the noise are Gaussian distributed. Then,MMSE estimators under speech presence uncertainty are derived. Furthermore,the proper estimators of the speech statistical parameters are proposed. The speech Laplacian factor is estimated by a new deci-sion-directed method. The simulation results show that the proposed algorithm yields less residual noise and better speech quality than the Gaussian based speech enhancement algorithms proposed in recent years.展开更多
The Chinese intelligence input technology, its applications, and a customer service call center system are developed. This technology can be used both in standard English telephone number input keyboard and in Chinese...The Chinese intelligence input technology, its applications, and a customer service call center system are developed. This technology can be used both in standard English telephone number input keyboard and in Chinese telephone number input keyboard .And authors develop sophisticated technologies including "Pinyin" (the Chinese phonetic alphabet ) encoding technology of phonetic symbol code and formal symbol code of Chinese character structure, phrase encoding technology, input technology of whole sentence intelligence encoding and input technology of Chinese telephone number encoding.展开更多
In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in th...In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.展开更多
基金the Natural Science Foundation of Jiangsu Province (No.BK2006001).
文摘In this paper,two speech enhancement systems with supergaussian speech modeling are presented. The clean speech components are estimated by Minimum-Mean-Square-Error (MMSE) es-timator under the assumption that the DCT coefficients of clean speech are modeled by a Laplacian or a Gamma distribution and the DCT coefficients of the noise are Gaussian distributed. Then,MMSE estimators under speech presence uncertainty are derived. Furthermore,the proper estimators of the speech statistical parameters are proposed. The speech Laplacian factor is estimated by a new deci-sion-directed method. The simulation results show that the proposed algorithm yields less residual noise and better speech quality than the Gaussian based speech enhancement algorithms proposed in recent years.
文摘The Chinese intelligence input technology, its applications, and a customer service call center system are developed. This technology can be used both in standard English telephone number input keyboard and in Chinese telephone number input keyboard .And authors develop sophisticated technologies including "Pinyin" (the Chinese phonetic alphabet ) encoding technology of phonetic symbol code and formal symbol code of Chinese character structure, phrase encoding technology, input technology of whole sentence intelligence encoding and input technology of Chinese telephone number encoding.
文摘In this paper the authors look into the problem of Hidden Markov Models （HMM）： the evaluation, the decoding and the learning problem. The authors have explored an approach to increase the effectiveness of HMM in the speech recognition field. Although hidden Markov modeling has significantly improved the performance of current speech-recognition systems, the general problem of completely fluent speaker-independent speech recognition is still far from being solved. For example, there is no system which is capable of reliably recognizing unconstrained conversational speech. Also, there does not exist a good way to infer the language structure from a limited corpus of spoken sentences statistically. Therefore, the authors want to provide an overview of the theory of HMM, discuss the role of statistical methods, and point out a range of theoretical and practical issues that deserve attention and are necessary to understand so as to further advance research in the field of speech recognition.
