## 3 Results and Discussions  
In order to evaluate and compare the performance of the proposed enhancement algorithm, three other algorithms are performed in this study, they are: traditional spectral subtraction method, basic Wiener filtering, and a noise-estimation algorithm [23]. For the purpose of analyzing the time-frequency distribution of the original/enhanced speech, speech spectrograms were provided since they have been identified as a well-suited tool for observing both the residual noise and speech distortion. In addition, results are also measured objectively by Signal-to Noise ratio (SNR) and subjectively by Mean Opinion Score (MOS) in conditions of different additive white Gaussian noise as well as Bobble noise (for MOS) for the algorithm evaluation. Figure 3 shows the spectrograms of the original EL speech (a), and the enhanced speech using traditional spectral subtraction algorithm (b), basic Wiener filtering (c), noise-estimation algorithm (d), and the proposed multi-band spectral subtraction algorithm in this study (e). The speech material is a Chinese sentence \"Xi An Jiao Tong Da Xue\" ('Xi'an Jiaotong University' in English). Figure 3: Spectrogram of the sentence “Xi An Jiao Tong Da Xue”. Due to its different speech production theory and working conditions, EL speech has some special attributes. As stated before, the most special is that radiated noise, the additive environment noise may also combined in the origin EL speech. These noises can be clearly seen from Figure 3(a), especially in speech-pause region. Figure 3(b) shows that the spectral subtraction algorithm is effective in reducing the radiated noises, both in the speech and non-speech section. However, there are still too much residual noise remains in the enhanced speech, especially in the high-frequency section, suggesting that the noise reduction is not satisfactory. Figures 3(c)-(d) indicate that much noise still remains in the EL speech enhanced by the basic Wiener filtering and the noise-estimation algorithm, suggest that the noise reduction is not satisfactory. The proposed algorithm appears to be much better in reducing radiated noise as shown in Figure 3(e), since which can not only greatly reduces the low-frequency noise, but also eliminates the high-frequency noise completely. Observing the speech-pause regions, the residual noise is almost absent. Moreover, it is clearly visible that the residual noise is reduced to a large extent and has lost its structure. These results suggest that the proposed algorithm achieves a better reduction of the whole-frequency noise for EL speech than other enhancement methods. The SNR measures were used for evaluation of the proposed multi-band spectral subtraction algorithm objectively. The results of SNR for the white noise experiments are shown in Figure 4, for five sentences produced by two male laryngectomees (these five SNRs are averaged). Methods compared included the traditional spectral subtraction algorithm (spectral subtraction), basic Wiener filtering (Wiener), noise-estimation algorithm (noise-estimation), and the proposed multi-band spectral subtraction algorithm (multi-band). From the figure, the proposed multi-band spectral subtraction method and the noise-estimation method have the best performance for this noise condition. Each of these gave about 6 dB improvement at the lower SNRs increasing to about 12 dB improvement at the higher SNRs than traditional spectral subtraction method. The multi-band method shows the best SNR improvement, and did slightly better than the noise-estimation method, especially for the higher SNR noise cases. Figure 4: SNR results for white noise case at -10, -5, 0, +5, and +10 dB SNR levels  
Subjective results using Mean Opinion Scores (MOS) for these enhancement algorithms for five sentences produced by two male laryngectomees (averaged) are shown in Figure 5. The noisy speech in the case of additive White and Babble noise has an input SNR of 0 dB.