The third ‘CHiME’ speech separation and recognition challenge: Dataset, task and baselines

TLDR

The CHiME challenge series seeks to advance far‑field speech recognition by presenting the 3rd CHiME Challenge, which evaluates ASR performance in a real‑world scenario where a person speaks to a tablet equipped with a six‑channel microphone array. The paper details data collection, task definition, and baseline systems for data simulation, audio enhancement, and recognition used in the challenge. The challenge attracted 26 submissions, with strategies that outperformed the MVDR array processing and DNN acoustic modeling reference, and underscored the role of simulated data in training and evaluation.

Abstract

The CHiME challenge series aims to advance far field speech recognition technology by promoting research at the interface of signal processing and automatic speech recognition. This paper presents the design and outcomes of the 3rd CHiME Challenge, which targets the performance of automatic speech recognition in a real-world, commercially-motivated scenario: a person talking to a tablet device that has been fitted with a six-channel microphone array. The paper describes the data collection, the task definition and the baseline systems for data simulation, enhancement and recognition. The paper then presents an overview of the 26 systems that were submitted to the challenge focusing on the strategies that proved to be most successful relative to the MVDR array processing and DNN acoustic modeling reference system. Challenge findings related to the role of simulated data in system training and evaluation are discussed.