Automatic speech polarity detection using phase information from complex analytic signal representations

The objective of the present work is to propose an automatic polarity detection algorithm for speech or electro-glottogram (EGG) using the phase information obtained from the complex analytic signals. The analytic signals (s_a(n)) are the complex time representation of the given signal derived using the Hilbert transform. The polarity of the signal is determined from the nature of the slope in the cosine phase of s_a(n) corresponding to the peaks in the magnitude of s_a(n) (Hilbert envelope). The effectiveness of the proposed algorithm is evaluated for speech and EGG utterances of CMU-Arctic database and German emotional speech database (Emo-DB). Also, the performance of the proposed method is found to be comparable with the recently proposed polarity detection algorithm based on residual excitation skewness.