A Robust Method Based on Likelihood Estimation for Speech Signial Detection

Speech signal detection is found to have a variety of applications in the speech communication. Many methods have been proposed for that purpose. Most of these methods can achieve very high detection accuracy for a reasonable given false alarm probability in clean speech environment. However, these methods become less reliable in the noisy environment. The accurate detection of speech signal is proven to be still very difficult in the presence of noise and interference. In this paper, we propose a method to use the likelihood estimated from a noise model to detect the speech signal. We shall address the problems on how to train a noise model, how to use the likelihood to detect the speech signal and how to use an on-line adaptation procedure to adapt the model parameters to a new noisy environment. We will also present experiment results to demonstrate some of the properties and advantages of the method.