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首页> 外文期刊>Cognitive Computation >Auditory-Inspired Morphological Processing of Speech Spectrograms: Applications in Automatic Speech Recognition and Speech Enhancement
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Auditory-Inspired Morphological Processing of Speech Spectrograms: Applications in Automatic Speech Recognition and Speech Enhancement

机译：语音频谱图的听觉启发式形态处理：在自动语音识别和语音增强中的应用

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摘要

New auditory-inspired speech processing methods are presented in this paper, combining spectral subtraction and two-dimensional non-linear filtering techniques originally conceived for image processing purposes. In particular, mathematical morphology operations, like erosion and dilation, are applied to noisy speech spectrograms using specifically designed structuring elements inspired in the masking properties of the human auditory system. This is effectively complemented with a pre-processing stage including the conventional spectral subtraction procedure and auditory filterbanks. These methods were tested in both speech enhancement and automatic speech recognition tasks. For the first, time-frequency anisotropic structuring elements over grey-scale spectrograms were found to provide a better perceptual quality than isotropic ones, revealing themselves as more appropriate—under a number of perceptual quality estimation measures and several signal-to-noise ratios on the Aurora database—for retaining the structure of speech while removing background noise. For the second, the combination of Spectral Subtraction and auditory-inspired Morphological Filtering was found to improve recognition rates in a noise-contaminated version of the Isolet database.
机译：本文提出了新的听觉启发式语音处理方法，结合了频谱减法和最初为图像处理目的而构想的二维非线性滤波技术。特别是，使用受人类听觉系统掩蔽特性启发而特别设计的结构化元素，将数学形态学运算（例如侵蚀和扩张）应用于嘈杂的语音频谱图。有效地补充了预处理阶段，包括传统的光谱减法程序和听觉滤波器组。这些方法已在语音增强和自动语音识别任务中进行了测试。首先，发现在灰阶频谱图上的时频各向异性结构元素提供了比各向同性更好的感知质量，并在多种感知质量估计方法和多种信噪比下显示出它们更合适。 Aurora数据库-用于保留语音结构，同时消除背景噪音。第二，在Isolet数据库的受噪声污染的版本中，发现将频谱减法与听觉启发式形态滤波相结合可以提高识别率。

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