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

Jon Barker, Shinji Watanabe, Emmanuel Vincent, Jan Trmal

Research output: Contribution to journalArticlepeer-review

Abstract

The CHiME challenge series aims to advance robust automatic speech recognition (ASR) technology by promoting research at the interface of speech and language processing, signal processing, and machine learning. This paper introduces the 5th CHiME Challenge, which considers the task of distant multi-microphone conversational ASR in real home environments. Speech material was elicited using a dinner party scenario with efforts taken to capture data that is representative of natural conversational speech and recorded by 6 Kinect microphone arrays and 4 binaural microphone pairs. The challenge features a single-array track and a multiple-array track and, for each track, distinct rankings will be produced for systems focusing on robustness with respect to distant-microphone capture vs. systems attempting to address all aspects of the task including conversational language modeling. We discuss the rationale for the challenge and provide a detailed description of the data collection procedure, the task, and the baseline systems for array synchronization, speech enhancement, and conventional and end-to-end ASR.

Original languageEnglish
JournalUnknown Journal
Publication statusPublished - 2018 Mar 28
Externally publishedYes

Keywords

  • Conversational speech
  • Index Terms: Robust ASR
  • Microphone array
  • Noise
  • Reverberation
  • ‘CHiME’ challenge

ASJC Scopus subject areas

  • General

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