Detection of Periodicity and Aperiodicity in Speech Signal Based on Temporal Information

In this paper, we discuss a direct measure for the proportion of periodic and aperiodic components in speech signals. Further, in the periodic regions, we estimate the pitch period. This method is particularly useful in situations where the speech signal contains simultaneous periodic and aperiodic energy, as in the case of breathy vowels and some voiced obstruents. The performance of this algorithm was evaluated on three different natural speech databases that have simultaneously recorded EGG data. The results show excellent agreement between the periodic/aperiodic decisions made by the algorithm presented here and the estimates obtained from the EGG data. To evaluate the efficiency of this algorithm in predicting pitch, reference pitch values were obtained from the EGG data using a simple peak-picking based algorithm. The gross error rate in pitch prediction was 6.1% for male subjects and 12.5% for female subjects.