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
On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative fe...On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative feedback of the neural cell in the output layer, the cell in the input layer excites the corresponding cell in the ontput layer meanwhile it inhibits the lateral cells. The network has its advantage on the processing of sound wave. In addition to filter the noise, it can search the significance frequency segments （Barks）. The ＂channel suppresser＂ feature, the special phenomena of the human ear, is explained based on the model. The learning algorithm of the network model is discussed, too. In the end, an example is introduced about the application of the network.展开更多
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.
基金Shanghai Natural Research Foundation (No.06dz15003)
文摘On the base of auditory neural system, the network model on the processing of the sound wave is presented. The mathematic equation of the network is also discussed. In the network model, in addition to the negative feedback of the neural cell in the output layer, the cell in the input layer excites the corresponding cell in the ontput layer meanwhile it inhibits the lateral cells. The network has its advantage on the processing of sound wave. In addition to filter the noise, it can search the significance frequency segments （Barks）. The ＂channel suppresser＂ feature, the special phenomena of the human ear, is explained based on the model. The learning algorithm of the network model is discussed, too. In the end, an example is introduced about the application of the network.
