An acoustic measure for predicting recognition performance degradation
Kazuya Takeda, Masanori Kondo, Fumitada Itakura · 2002
An acoustic measure for predicting the degradation of speech recognition performance due to noise contamination is developed. The merits of the proposed measure over using conventional SNR are that (1) the measure does not require the original clean signal as a reference signal (2) the measure takes the spectral shape of the noise into account and, (3) the measure can predict recognition performance directly. The basic idea of the measure is to utilize the dynamic range of the sub-band signals as an estimate of SNR in the corresponding subband and, to predict the degradation of the recognition performance by taking a product of the recognition accuracy of each sub-band. The proposed measure is tested through experimental evaluation using white Gaussian and human speech like (HSL) noise. In the experiment, the correlation between the predicted and the actual recognition accuracies are 0.96 and 0.99 for white and HSL noise respectively. From the results, the effectiveness of the proposed measure is confirmed.