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On-line speaking rate estimation using Gaussian mixture models

Gaussian mixture models (GMM) are a widespread tool in applications like speaker identification or verification. In contrast to hidden Markov models (HMM) Gaussian mixture models are designed to model the general properties of an underlying acoustic source. In our paper we extend the application of GMMs to the assessment of speaking rate. Directly trained on the acoustic data, they can be either applied directly to estimate the speech rate category or-with the help of a mapping function-they can provide a continuous measure for the speaking rate. The mapping function can be realized by means of a neural net. First experiments showed a correlation coefficient of 0.66 between the lexical phoneme rate and our estimation based on speech rate dependent spectral variation. Moreover, our approach can be used simultaneously for high accuracy on-line gender detection.