# Whispered Speech Conversion Based on the Inversion of Mel Frequency Cepstral Coefficient Features
## 2 Whispered Speech Conversion Process
### Inversion of Speech Features
In the next experiment, we examined the performance of the proposed method both subjectively and objectively. The subjective test mainly used the auditory characteristics of the human ear to evaluate the converted speech from multiple aspects, such as propensity, intelligibility, and naturalness, using the mean opinion score (MOS) scale. The MOS scale categorizes speech quality into five levels, i.e., 1: Bad, unbearable annoying; 2: Poor, obviously distorted and annoying; 3: Fair, perceivably distorted and slightly annoying; 4: Good, slightly distorted; 5: Excellent, no perceivable distortion. The subjects were asked to score the converted speech based on the above scale to assess its quality. For the objective test, we used the cepstral distortion (CD), which is calculated by the following formula:

\[CD=\frac{10}{\log 10}\sqrt{2\sum_{d=1}^{D}\left(C_{d}-{C_{d}}^{\prime}\right)^{2}} \tag{14}\]

where \(C_{d}\) and \({C_{d}}^{\prime}\) are the \(d\)th dimensional Mel frequency cepstra of the converted speech and the target speech, respectively, and \(d\) is the number of speech frames. The average value of the frames is used as the CD value of the speech segment. The number of Gaussian components of GMM (M) must be determined first, and it is difficult to deduce the optimal M theoretically; however, that number can be experimentally obtained based on different data sets. It is generally set to 4, 8, 16, etc. In this study, from the results in Figure 4, we found the optimal conditions are as follows: multiframe speech and M = 32. Thus, in subsequent experiments, these conditions were adopted. ### Experimental Results

When using the MOS score for a subjective evaluation, the higher the MOS score, the higher the intelligibility and naturalness of the converted speech. In the MOS evaluation, we invited five testers to score 10 converted speeches from each conversion direction. The results are shown Table 1 in which LPC [31] was used to extract partial correlation coefficients and build signal generation models for synthesis, the network configurations of DNN-based [11] were 60-120-60-120-60, MFCC represents the result from using only the current frame for feature conversion, and MFCCs represents the result from using both the current frame and the previous and subsequent frames (i.e., multiple frames) for feature conversion. Figure 4: Parameter selection curve. The results in Table 1 show that using multiple frames is more effective than using a single frame. At the same time, the MOS scores of the converted whispered speech using the MFCC feature inversion are all greater than 3, indicating the conversion is very effective in recognizing whispered speech. CD is an objective evaluation standard, especially for evaluating the performance of cepstral feature conversion. According to the formula, the smaller the distance (d), the closer the converted speech to normal speech. As shown in Figure 5, the speech reconstructed using the proposed method was more similar to normal speech, indicating that the feature inversion algorithm is a feasible method for whispered speech conversion. To more visually show the conversion effectiveness, a spectrogram case is shown in Figure 6, in which the speech sentence is \"gu min sang shi xin xin\" (\"shareholders have lost confidence\"). The data in Figure 6 show that the MFCC feature inversion-based method proposed in this study achieved a better conversion between formant and spectral envelope. The whispered speech is a voiced sound with an energy approximately 20 dB lower than that of normal speech; however, the sound energy is increased significantly in the converted speech. The speech converted using the proposed method also exhibits a clearer harmonic phenomenon than whispered speech, with significant horizontal bars of the harmonics and distinct alternation of voiced and unvoiced sounds, indicating that the spectrogram of the converted speech is more similar to that of normal speech.