Monaural speech separation and recognition challenge
Monaural speech separation and recognition challenge
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DOI：
10.1016/j.csl.2009.02.006
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发表时间：
2010-01-01
影响因子：
4.3
通讯作者：
Rennie, Steven J.
中科院分区：
文献类型：
--
作者：
Cooke, Martin;Hershey, John R.;Rennie, Steven J.
日常条件下的健壮语音识别需要解决许多具有挑战性的问题，尤其是处理多个声源的能力。几十年来，人们一直在研究在竞争说话者在场的情况下进行语音识别的具体情况，产生了许多截然不同的算法解决方案，其重点从对目标和竞争语音建模到使用听觉分组原则的语音分离。单声道语音分离和识别挑战的目的是允许对竞争说话者问题的技术进行大规模比较。任务是识别目标说话者说出的句子中的关键词，当目标说话者与背景说话者说类似句子时，将其混合到一个单一频道中。除了基线识别系统外，还提供了10组独立的结果。使用共同的培训和测试数据以及共同的衡量标准来评估绩效。听者在同一项任务中的表现也被测量了。本文描述了挑战问题，比较了各种算法的性能，并讨论了区分不同系统的因素。比较的一个亮点是发现几个系统在某些条件下取得了接近人类的表现，其中一个系统总体上超过了听众。(C)2009爱思唯尔有限公司。保留所有权利。
Robust speech recognition in everyday conditions requires the solution to a number of challenging problems, not least the ability to handle multiple sound sources. The specific case of speech recognition in the presence of a competing talker has been studied for several decades, resulting in a number of quite distinct algorithmic solutions whose focus ranges from modeling both target and competing speech to speech separation using auditory grouping principles. The purpose of the monaural speech separation and recognition challenge was to permit a large-scale comparison of techniques for the competing talker problem. The task was to identify keywords in sentences spoken by a target talker when mixed into a single channel with a background talker speaking similar sentences. Ten independent sets of results were contributed, alongside a baseline recognition system. Performance was evaluated using common training and test data and common metrics. Listeners' performance in the same task was also measured. This paper describes the challenge problem, compares the performance of the contributed algorithms, and discusses the factors which distinguish the systems. One highlight of the comparison was the finding that several systems achieved near-human performance in some conditions, and one out-performed listeners overall. (C) 2009 Elsevier Ltd. All rights reserved.