Robust speech recognition for car environment noise
Robust speech recognition for car environment noise
复制标题
针对汽车环境噪声的鲁棒语音识别
DOI：
10.1002/ecjc.10055
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
N. Hataoka
中科院分区：
文献类型：
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作者：
H. Kokubo;A. Amano;N. Hataoka
介绍了一种以SuperH（SH）Micon为平台的汽车信息机中的噪声处理方法。使用噪声声学模型和谱减法方案，该方法通过计算机模拟来评估对通过考虑使用环境和麦克风位置构建的评估数据库。在单独使用噪声模型的评价中，当使用由没有噪声的语音指导的HMM时，在测试轨道驾驶环境中获得11.0%的语音识别率，而当使用通过在指导语音上叠加噪声而构建的HMM时，获得82.7%的识别率。语音识别率提高了约额外的5%，87.6%，通过使用这种含噪声的声学模型和谱减法方案。此外，当语音识别中间件与抗噪声的方法被加载到SH板上，识别性能与计算机模拟结果相媲美。© 2002 Wiley Periodicals，Inc. Electron Comm Jpn Pt 3，85（11）：65-73，2002;在线发表于Wiley InterScience（www.interscience.wiley.com）。DOI 10.1002/ecjc.10055
A method for handling noise in a car information machine incorporating speech recognition middleware and having a SuperH (SH) Micon as its platform is reported. Using both a noise acoustic model and a spectral subtraction scheme, this method is evaluated by computer simulation against an evaluation database constructed by considering the usage environment and the microphone position. In evaluations using the noise model alone, a speech recognition rate of 11.0% in a test track driving environment was obtained when an HMM tutored by speech without noise was used, while a recognition rate of 82.7% was obtained when an HMM constructed by superimposing noise over the tutoring speech was used. The speech recognition rate was improved by about an additional 5%, to 87.6%, by using this noise-containing acoustic model and the spectral subtraction scheme. In addition, when speech recognition middleware incorporated with the antinoise method was loaded on an SH board, a recognition performance comparable with the computer simulation results was obtained. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 85(11): 65–73, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.10055