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Acoustic Feature Optimization Based on F-Ratio for Robust Speech Recognition

机译:基于F比率的声学特征优化用于鲁棒语音识别

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This paper focuses on the problem of performance degradation in mismatched speech recognition. The F-Ratio analysis method is utilized to analyze the significance of different frequency bands for speech unit classification, and we find that frequencies around 1 kHz and 3 kHz, which are the upper bounds of the first and the second formants for most of the vowels, should be emphasized in comparison to the Mel-frequency cep-stral coefficients (MFCC). The analysis result is further observed to be stable in several typical mismatched situations. Similar to the Mel-Frequency scale, another frequency scale called the F-Ratio-scale is thus proposed to optimize the filter bank design for the MFCC features, and make each subband contains equal significance for speech unit classification. Under comparable conditions, with the modified features we get a relative 43.20% decrease compared with the MFCC in sentence error rate for the emotion affected speech recognition, 35.54%, 23.03% for the noisy speech recognition at 15 dB and 0 dB SNR (signal to noise ratio) respectively, and 64.50% for the three years' 863 test data. The application of the F-Ratio analysis on the clean training set of the Aurora2 database demonstrates its robustness over languages, texts and sampling rates.
机译:本文着重于不匹配语音识别的性能下降问题。 F比率分析方法用于分析不同频带对语音单位分类的重要性,我们发现大约1 kHz和3 kHz的频率是大多数元音的第一和第二共振峰的上限与梅尔频率倒谱系数(MFCC)相比,应强调。进一步观察到分析结果在几种典型的不匹配情况下是稳定的。与梅尔频率标度类似,因此提出了另一个频率标度,称为F比例标度,以优化MFCC功能的滤波器组设计,并使每个子带对于语音单元分类具有相同的重要性。在可比较的条件下,与MFCC相比,在经过修改的功能下,情感影响的语音识别的句子错误率与MFCC相比降低了43.20%,在15 dB和SNR为0 dB的嘈杂语音识别中,错误率分别为35.54%和23.03%(噪声比)和三年863测试数据的64.50%。 F-Ratio分析在Aurora2数据库的干净训练集上的应用证明了它对语言,文本和采样率的鲁棒性。

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