Abstract
This paper presents a sound source (talker) localization method using only a single microphone, where a Gaussian Mixture Model (GMM) of clean speech is introduced to estimate the acoustic transfer function from a user's position. The new method is able to carry out this estimation without measuring impulse responses. The frame sequence of the acoustic transfer function is estimated by maximizing the likelihood of training data uttered from a given position, where the cepstral parameters are used to effectively represent useful clean speech. Using the estimated frame sequence data, the GMM of the acoustic transfer function is created to deal with the influence of a room impulse response. Then, for each test dataset, we find a maximum-likelihood (ML) GMM from among the estimated GMMs corresponding to each position. The effectiveness of this method has been confirmed by talker localization experiments performed in a room environment.
Highlights
Many systems using microphone arrays have been tried in order to localize sound sources
In our previous work [11, 12], we proposed Hidden Markov Model (HMM) separation for reverberant speech recognition, where the observed speech is separated into the acoustic transfer function and the clean speech HMM
We introduce a Gaussian Mixture Model (GMM) of the acoustic transfer function to deal with the influence of a room impulse response
Summary
Many systems using microphone arrays have been tried in order to localize sound sources. In our previous work [11] for reverberant speech recognition, HMM separation required texts of a user’s utterances in order to estimate the acoustic transfer function. The acoustic transfer function is estimated from observed (reverberant) speech using a clean speech model without having to rely on user utterance texts, where a Gaussian Mixture Model (GMM) is used to model clean speech features. This estimation is performed in the cepstral domain employing an approach based upon maximum likelihood (ML). The results of our talkerlocalization experiments show the effectiveness of our method
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