An Initialization Scheme for Meeting Separation with Spatial Mixture Models

TL;DR

This paper introduces a novel initialization method for spatial mixture models in meeting separation, leveraging the fact that only one speaker is active at a time, leading to improved speech recognition accuracy.

Contribution

The paper proposes a new initialization scheme for spatial mixture models that enhances meeting separation performance by utilizing temporal speaker activity patterns.

Findings

Achieves a WER of 5.9% on LibriCSS, comparable to the best results.

Significantly reduces WER compared to random initialization.

Provides a spatial diarization method based on the mixture model.

Abstract

Spatial mixture model (SMM) supported acoustic beamforming has been extensively used for the separation of simultaneously active speakers. However, it has hardly been considered for the separation of meeting data, that are characterized by long recordings and only partially overlapping speech. In this contribution, we show that the fact that often only a single speaker is active can be utilized for a clever initialization of an SMM that employs time-varying class priors. In experiments on LibriCSS we show that the proposed initialization scheme achieves a significantly lower Word Error Rate (WER) on a downstream speech recognition task than a random initialization of the class probabilities by drawing from a Dirichlet distribution. With the only requirement that the number of speakers has to be known, we obtain a WER of 5.9 %, which is comparable to the best reported WER on this data set. Furthermore, the estimated speaker activity from the mixture model serves as a diarization based on spatial information.