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

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.

An analysis of environment, microphone and data simulation mismatches in robust speech recognition

“…The start and end time and the speaker identity of all utterances are annotated and the task is to transcribe the real test utterances. For more details, see Barker et al (2015).…”

“…To start with, let us analyze the performance of the baseline ASR backend for the CHiME-3 challenge (Barker et al, 2015). Two different acoustic models are considered.…”

“…Research in this field has made great progress thanks to real speech corpora collected for various application scenarios such as voice command for cars (Hansen et al, 2001), smart homes (Ravanelli et al, 2015), or tablets (Barker et al, 2015), and automatic transcription of lectures (Lamel et al, 1994), meetings (Renals et al, 2008), conversations (Harper, 2015), dialogues (Stupakov et al, 2011), game sessions (Fox et al, 2013), or broadcast media (Bell et al, 2015). In most corpora, the training speakers differ from the test speakers.…”

“…In the REVERB dataset (Kinoshita et al, 2013), the speaker distances for real and simulated data differ, which does not allow fair comparison. The CHiME-3 dataset (Barker et al, 2015) provides a data simulation tool which aims to reproduce the characteristics of real data for training and twinned real and simulated data pairs for development and testing. This makes it possible to evaluate the improvement brought by training on simulated data in addition to real data and to compare the performance on simulated vs. real test data for various techniques.…”

Investigation and Development of Methods for Improving Robustness of Automatic Speech Recognition Algorithms in Complex Acoustic Environments

“…Development of such processing methods is especially important for the applications like "smart home" when microphones are located in several parts of the room and both location and orientation of speaker's head are unknown in advance. In order to promote the development of multi-microphone approaches in robust speech recognition, several international competitions like CHiME (Computational Hearing in Multisource Environments) Challenge [6][7][8][9] have been organized in recent years.…”