The PASCAL CHiME speech separation and recognition challenge
The PASCAL CHiME speech separation and recognition challenge
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DOI：
10.1016/j.csl.2012.10.004
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发表时间：
2013-05-01
影响因子：
4.3
通讯作者：
Green, Phil
中科院分区：
文献类型：
--
作者：
Barker, Jon;Vincent, Emmanuel;Green, Phil
远距离麦克风语音识别系统的操作与人类一样的鲁棒性仍然是一个遥远的目标。关键困难在于，在日常收听条件下操作需要处理混响混合到由多个竞争声源组成的噪音背景中的语音信号。本文介绍了最近的语音识别评估，旨在汇集来自多个社区的研究人员，以促进新的方法来解决这个问题。这项任务是在一个忙碌的家庭环境中，从混在音频背景中的句子中识别关键词。这项挑战的目的是模拟多学科环境问题的基本困难，同时保持一个规模，使其能够为广大受众所了解。与先前的ASR评估相比，该任务的一个特别新奇在于，要识别的话语是在连续的音频背景中提供的，而不是作为预先分段的话语，从而允许采用一系列的背景建模技术。该挑战吸引了13个提交。本文介绍了挑战性的问题，提供了一个系统的概述，并提供了一个比较旁边的基线识别系统和人类的表现。本文讨论了从挑战中获得的见解以及为今后设计此类评价所吸取的经验教训。(c)2012爱思唯尔有限公司保留所有权利。
Distant microphone speech recognition systems that operate with human-like robustness remain a distant goal. The key difficulty is that operating in everyday listening conditions entails processing a speech signal that is reverberantly mixed into a noise background composed of multiple competing sound sources. This paper describes a recent speech recognition evaluation that was designed to bring together researchers from multiple communities in order to foster novel approaches to this problem. The task was to identify keywords from sentences reverberantly mixed into audio backgrounds binaurally recorded in a busy domestic environment. The challenge was designed to model the essential difficulties of the multisource environment problem while remaining on a scale that would make it accessible to a wide audience. Compared to previous ASR evaluations a particular novelty of the task is that the utterances to be recognised were provided in a continuous audio background rather than as pre-segmented utterances thus allowing a range of background modelling techniques to be employed. The challenge attracted thirteen submissions. This paper describes the challenge problem, provides an overview of the systems that were entered and provides a comparison alongside both a baseline recognition system and human performance. The paper discusses insights gained from the challenge and lessons learnt for the design of future such evaluations. (c) 2012 Elsevier Ltd. All rights reserved.