Adding Voicing Features into Speech Recognition Based on HMM in Slovak

This article discusses the impact of substituting some of the basic speech features with the voiced/ unvoiced information and possibly with the estimated pitch value. As a good measure of the signal's voicing the average magnitude difference function was assumed, especially the ratio of its average value to its local minima found within the accepted ranges of the pitch. Furthermore, the pitch itself was used as an auxiliary feature to the base MFCC and PLP features. Experiments were performed on the professional database SPEECHDAT-SK for mobile applications working in harsh conditions, using various HMM models of context dependent and independent phonemes. All models were trained following the MASPER training scheme. In all cases the voicing feature brought improved results by more than 9% compared to the base systems. However the role of the pitch itself in the case of speaker independent ASR system evaluated over different tasks was not always so beneficial.