Speech in Noisy Environments: robust automatic segmentation, feature extraction, and hypothesis combination
Speech in Noisy Environments: robust automatic segmentation, feature extraction, and hypothesis combination
复制标题
嘈杂环境中的语音：鲁棒的自动分割、特征提取和假设组合
DOI：
10.1109/icassp.2001.940820
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
R. Stern
中科院分区：
文献类型：
--
作者：
Rita Singh;M. Seltzer;B. Raj;R. Stern
2000年8月，海军研究实验室（NRL）对噪声环境中的语音（SPINE 1）进行了首次评估。评估的目的是测试现有的核心语音识别技术在不同类型和级别的噪音中的语音。在这种情况下，噪音是从军事设置。卡内基梅隆大学成功设计的系统所使用的策略包括会话自适应分割、用于计算cepstra的稳健的Mel尺度滤波、使用并行前端特征和噪声补偿算法以及通过词图进行并行假设组合。本文介绍了这些组件的设计决策背后的动机，通过观察和实验的支持。
The first evaluation for Speech in Noisy Environments (SPINE1) was conducted by the Naval Research Labs (NRL) in August, 2000. The purpose of the evaluation was to test existing core speech recognition technologies for speech in the presence of varying types and levels of noise. In this case the noises were taken from military settings. Among the strategies used by Carnegie Mellon University's successful systems designed for this task were session-adaptive segmentation, robust mel-scale filtering for the computation of cepstra, the use of parallel front-end features and noise-compensation algorithms, and parallel hypotheses combination through word-graphs. This paper describes the motivations behind the design decisions taken for these components, supported by observations and experiments.