## 3 Results and Discussions
It can be seen from figure that the score of the enhanced speech obtained from using the multi-band algorithm is the highest, followed by that from the noise estimation and the Wiener filtering algorithms. This is true for both the original speech and the noisy speech. There is an interesting difference between the MOS results and the SNR results. The noise estimation algorithm received higher scores than would have been suggested by SNR, especially for the Babble noise, its score is little higher than the proposed method. The most likely explanation for this phenomenon is that the presence or absence of the residual musical noise and speech distortion in the enhanced speech, which are known to have significant impact on perception but have only mild influence on SNR values. It also can be seen from the speech spectrograms (Figure 3) that the spectrogram of the noise-estimation algorithm is more \"smooth\" than that from the proposed algorithm, which means less residual musical noise and speech distortion. Actually, the noise-estimation algorithm may suit for highly non-stationary noise environments, and the proposed algorithm may suit for colored noise environments. The results of the perceptual experiment also suggested that the enhanced speech with the proposed multi-band spectral subtraction algorithm is more pleasant, the residual noise is better reduced, and with minimal, if any, speech distortion. This is because that the over-subtraction factor of each frequency band can be adjusted, which can prevent speech from quality deterioration during spectral subtraction process. Figure 5: Acceptability scores of the original and enhanced EL speech. The noisy speech in the case of additive noise has an input SNR of 0 dB. It can be seen from theory of the multi-band algorithm that the number of bands may have important effects on the quality of enhanced speech. Therefore, we varied the number of bands from 1 to 10 in this study to determine the optimal number of bands. Linear frequency spacing was used, and the performed speech quality was examined using Subjective measures (MOS). Figure 6 plots the averaged MOS scores for five enhanced sentences (produced by two male laryngectomees) for different number of bands. It can be seen from the figure that the MOS score has a great improvement when the number of bands increased from 1 to 5 for both 5 and 0 dB. For the bands larger than 5, the score does show a slight increase in performance, however, it is too slight to perceivable in speech quality. Therefore, the optimal number of bands can be determined as 5 for EL speech. This number is almost consistent with the result of [PERSON] _et al._, which for normal speech was 4. In addition, when the total number of bands is one, then the approach of multi-band spectral subtraction algorithm reduces to the traditional power spectral subtraction approach[20]. Because the subtraction parameters are fixed for a given frame, the traditional spectral subtraction algorithm cannot reduce the noise effectively, especially for the colored noise. These limitations will be worse for the enhancement of EL speech in the case of combined electronic noise. With regard to the multi-band spectral subtraction algorithm, the over-subtraction factor of each frequency band can be adjusted, so that this algorithm can realize a good tradeoff between reducing noise, increasing intelligibility, and keeping the distortion acceptable to a human listener. The results also indicate that  
Figure 6: Acceptability scores of the performance of the multi-band spectral subtraction approach for different number of bands for the enhanced speech. The noisy speech in the case of additive White noise has an input SNR of 5 and 0 dB. the proposed algorithm can not only reduce the residual noise but also improve the low-frequency deficit of EL speech.