LibriheavyMix: A 20,000-Hour Dataset for Single-Channel Reverberant Multi-Talker Speech Separation, ASR and Speaker Diarization

TL;DR

This paper introduces LibriheavyMix, a large-scale dataset for single-channel reverberant multi-talker speech tasks, along with a benchmark pipeline for separation, recognition, and diarization in complex environments.

Contribution

It provides a new extensive dataset and a comprehensive pipeline benchmark for advancing single-channel multi-talker speech processing in reverberant settings.

Findings

Dataset effectively supports speech separation, recognition, and diarization research.

Benchmark pipeline demonstrates applicability across various speech processing tasks.

Evaluations confirm dataset's usefulness in real-world reverberant environments.

Abstract

The evolving speech processing landscape is increasingly focused on complex scenarios like meetings or cocktail parties with multiple simultaneous speakers and far-field conditions. Existing methodologies for addressing these challenges fall into two categories: multi-channel and single-channel solutions. Single-channel approaches, notable for their generality and convenience, do not require specific information about microphone arrays. This paper presents a large-scale far-field overlapping speech dataset, crafted to advance research in speech separation, recognition, and speaker diarization. This dataset is a critical resource for decoding ``Who said What and When'' in multi-talker, reverberant environments, a daunting challenge in the field. Additionally, we introduce a pipeline system encompassing speech separation, recognition, and diarization as a foundational benchmark. Evaluations on the WHAMR! dataset validate the broad applicability of the proposed data.