A novel method for computation of periodicity, aperiodicity and pitch of speech signals

The paper presents improvements to our previously proposed algorithm to compute the proportion of periodic and aperiodic energies in speech signals and to estimate the pitch period. Although previously the periodic and aperiodic energies were estimated independently of each other at each frame, a binary decision was made at each of the non-silent channels. We present an extension that replaces the binary decision with a measure of the degree of periodicity and aperiodicity in each channel. Evaluation on synthetic speech-like data shows a better agreement in the estimated SNR and the actual SNR by using this improvement. Moreover, in the task of estimating the SNRs, this method significantly outperforms a method based on cepstral coefficients. When the method is evaluated on a speech database, the periodicity and aperiodicity accuracy increase significantly. The previous pitch detector was prone to committing pitch doubling and pitch halving errors and was unable to detect pitch reliably in weakly periodic regions. Significant changes have reduced the error rate by 28.7%. The pitch detector is also able to detect accurately the pitch of the synthetic speech-like signals and to capture the jitter present in the signals.