This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature i...This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature in speech recognition. The duration distribution based hidden Markov module in a speaker independent large vocabulary mandarin speech recognition system was reconstructed from the feature vectors in the front-end detection stage. The goal was to improve the performance of the existing system by combining new features to the baseline feature vector. This paper also deals with errors associated with using a pre-emphasis filter in the front end processing of the present scheme, which causes an increase in the noise energy at high frequencies above 4 kHz and in some cases degrades the recognition accuracy. The experimental results show that eliminating the pre-emphasis filters from the pre-processing stage and using NLTFD with compensated DTEO combined with Mel frequency cepstrum components give a 21.95% reduction in the relative error rate compared to the conventional technique with 25 candidates used in the test.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
基金the National High- Tech Research andDevelopm ent Program of China(No. 2 0 0 1AA114 0 71)
文摘This work demonstrates the use of the nonlinear time-frequency distribution (NLTFD) of a discrete time energy operator (DTEO) based on amplitude modulation-frequency modulation demodulation techniques as a feature in speech recognition. The duration distribution based hidden Markov module in a speaker independent large vocabulary mandarin speech recognition system was reconstructed from the feature vectors in the front-end detection stage. The goal was to improve the performance of the existing system by combining new features to the baseline feature vector. This paper also deals with errors associated with using a pre-emphasis filter in the front end processing of the present scheme, which causes an increase in the noise energy at high frequencies above 4 kHz and in some cases degrades the recognition accuracy. The experimental results show that eliminating the pre-emphasis filters from the pre-processing stage and using NLTFD with compensated DTEO combined with Mel frequency cepstrum components give a 21.95% reduction in the relative error rate compared to the conventional technique with 25 candidates used in the test.
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.
