Many-Speakers Single Channel Speech Separation with Optimal Permutation Training

Code

• github.com/shakeddovrat/librimix
  OfficialIn papernone★ 6

Abstract

Single channel speech separation has experienced great progress in the last few years. However, training neural speech separation for a large number of speakers (e.g., more than 10 speakers) is out of reach for the current methods, which rely on the Permutation Invariant Loss (PIT). In this work, we present a permutation invariant training that employs the Hungarian algorithm in order to train with an O(C^3) time complexity, where C is the number of speakers, in comparison to O(C!) of PIT based methods. Furthermore, we present a modified architecture that can handle the increased number of speakers. Our approach separates up to 20 speakers and improves the previous results for large C by a wide margin.

Tasks

Benchmark Results

Reproductions