Multiband statistical learning for f/sub 0/ estimation in speech
We investigate a simple algorithm that combines multiband processing and least squares fits to estimate f/sub 0/ contours in speech. The algorithm is untraditional in several respects: it makes no use of FFTs or autocorrelation at the pitch period; it updates the pitch incrementally on a sample-by-sample basis; it avoids peak picking and does not require interpolation in time or frequency to obtain high resolution estimates; it works reliably, in real time, without the need for postprocessing to produce smooth contours. We show that a baseline implementation of the algorithm, though already quite accurate, is significantly improved by incorporating a model of statistical learning into its final stages. Model parameters are estimated from training data to minimize the likelihood of gross errors in f/sub 0/, as well as errors in classifying voiced versus unvoiced speech. Experimental results on several databases confirm the benefits of statistical learning.