A posteriori voiced/unvoiced probability estimation based on a sinusoidal model

In this paper, we focus on methods for estimating the a posteriori probability of a signal segment being voiced which employ a harmonic signal model. Fisher et al. [1] present two likelihood functions for voiced and unvoiced speech from which the posterior probability can be derived. However, due to the chosen models, the a posteriori probability of a signal segment being voiced does not go to 0 % in unvoiced speech. Thus, a novel algorithm is proposed, which incorporates the expected unvoiced speech energy and allows for obtaining low probabilities. Further, it explicitly models the statistics of the segment energy and employs a state-of-the-art noise tracker. Experiments which were conducted on the TIMIT database for different noise types and noise levels show that the proposed method results in lower over-estimation and under-estimation of the voicing probability as compared to [1].