Effectiveness of polarity detection for improved epoch extraction from speech

Abstract

The objective of the present work is to demonstrate the significance of speech polarity detection in improving the accuracy of the estimated epochs in speech. The paper also proposes a method to extract the speech polarity information using the properties of the Hilbert transform. The Hilbert transform of the speech is computed as the imaginary part of the complex analytic signal representation of the original speech. The Hilbert envelope (HE) is then computed as the magnitude of the analytic signal. The average slope of the signal amplitudes of speech and Hilbert transform of speech around the peaks in the HE are observed to be varying in accordance with the polarity of the speech signal. The effectiveness of the proposed approach is confirmed by the performance evaluation over 7 voices of the phonetically balanced CMU-Arctic database and German emotional speech database. The performance of the proposed approach is also observed to be comparable with that of the existing algorithms such as residual skewness based polarity detection and Hilbert phase based speech polarity detection. Finally, a significant improvement in the identification accuracies of the estimated epochs in speech using the popular zero frequency filtering (ZFF) method is demonstrated as an application of the speech polarity detection.