Single-Channel Speech Separation Integrating Pitch Information Based on a Multi Task Learning Framework

Abstract

Pitch is a critical cue for speech separation in humans’ auditory perception. Although the technology of tracking pitch in single-talker speech succeeds in many applications, it’s still a challenging problem to extract pitch information from speech mixtures in machine perception. In this paper, we aimed to combine speech separation and pitch tracking together to let them benefit from each other. A multi-task learning framework was proposed, in which a unified objective that considered both speech separation and pitch tracking was used, based on the utterance-level permutation invariant training (uPIT) as well as deep clustering (DPCL). In such framework, two tasks were optimized simultaneously and could benefit from each other through the sharing layers in the networks. Experimental results indicated the proposed multi-task framework outperformed the corresponding single-task framework, in terms of both speech separation and pitch tracking. The improvement was more significant for challenging same-gender mixtures.