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This paper proposes a new algorithm to discriminate between speech and non-speech audio segments. It is intended for security applications as well as talker location identijcation in audio conferencing systems, equipped with microphone arrays.The proposed method is based on splitting the audio segment into small frames and detecting the presence of pitch in each one of them. The ratio of frames with pitch detected to the total number of frames is dejned as the pitch ratio and is used as the main feature to class& speech and non-speech segments. The performance of the proposed method is evaluated using a libraly of audio segments containing female and male speech, and nonspeech segments such as computer fan noise, cocktail noise, footsteps, and traffic noise.It is shown that the proposed algorithm can achieve correct decision of97% for the speech and 98% for nonspeech segments, 0.5-seconds long.

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In this paper, we propose a robust talker identification (TI) system that can identify speakers in different reverberant environments. We describe a reverberation sensing system (RSS) that determines the approximate reverberation level of the surrounding environment, and then selects a TI engine trained in a similar reverberant environment; greater TI accuracy is achieved when training and test environments are similar. In this system, there are six TI engines trained in five reverberant environments and one nonreverberant environment. Performance is evaluated using new twelve test environments, which are all within a reverberation time (RT60) range of 0 to 1.8 seconds.The TI system using the RSS achieves average identification accuracy of 95.5%; an enhancement of 9% over the TI engine trained in a non-reverberant environment.