## 4 Experimental Results
### Experiment Results of SDS for Monosyllabic Vocabulary  
The speech discrimination scores of the six subjects using monosyllabic vocabulary in \"Mandarin Chinese Speech test CD\" were tested. As shown in Table 4, the contribution of the different frequency components varies in speech recognition [24; 25]. In Chinese, energy of the voicedess consonant is mainly focused on the medium-high frequency range. The loss of medium-high frequency sensitivity and resolution therefore leads to difficulty for voices consonant recognition.  
The experiments of SDS measurement based on both the original monosyllabic vocabulary and the processed monosyllabic vocabulary were also performed. Both the vocabularies included 30 equivalent wordlists. In every wordlist, there were 25 Chinese monosyllabic words. The tested original wordlists were selected randomly and the processed wordlists were always selected to ensure that they differed from the original wordlists. In addition, different lists were used in training and testing to ensure that the subjects were not able to remember the content. All six of the subjects utilized the supra-aural headphone in the silent room to listen to the words in the list. Equivalent monosyllabic wordlists were tested in different sound pressure levels from 10 dB to 100 dB with 10 dB steps. Spline interpolation was performed to acquire smooth performance-intensity curves. For every subject, nearly 100 min were spent to get performance-intensity (P-I) curves for the original vocabulary wordlists and the processed vocabulary wordlists.  
The curves of performance-intensity (P-I), which depict the relationship between speech discrimination score and speech intensity, are shown in Figure 6. In the figure, the relationship based on the original vocabulary is depicted by solid lines, while that based on the processed vocabulary is depicted by dashed lines. From the figure, it can be concluded that different SDS lines could be achieved even if the subjects had similar audigrams. For example, S1 and S3 had similar audigrams, but the maxima speech discrimination score for S1 was 82%, while the maxima score for S3 was 74%. The P-I curves of S5 reached the maxima SDS when the sound pressure level was 80 dB, and then declined with an increase of sound pressure level. This indicated that the cause of the deafness may be in the auditory central nervous system [26]. In general, it can be seen that the proposed piecewise-linear frequency shifting algorithm had essentially improved the SDS of six subjects in different sound pressure levels (SPLs) by 4%-10%. Notably, S5 achieved the most significant improvement.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency Range/Hz** & **Contribution Score \%** \\ \hline
0\(\sim\)250 & 2 \\
250\(\sim\)500 & 3 \\
500\(\sim\)1000 & 35 \\
1000\(\sim\)2000 & 35 \\
2000\(\sim\)4000 & 13 \\
4000\(\sim\)8000 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The contribution of frequency components for speech recognition.  
From P-I curves, the speech reception threshold (SRT) is defined, which is the softest intensity spondee words that an individual can repeat at least 50% of the time. The SRT of the six subjects with vocabularies before and after processing is listed in Table 5. From Table 5, the SRT of S1, S2, S5, and S6 were reduced by 3-8 dB, where the reduction of S2's SRT was the largest. It can be concluded that the enhancement of frequency discrimination in 3000 Hz and 4000 Hz had a good effect on the speech intelligibility of the subjects. In addition, the subjects had no obvious discomfort with the processed words and their ability to identify voiceless consonants was improved.  
Figure 6: Performance-intensity (P-I) curves using the original and the processed vocabularies with six subjects: (**a**) S1; (**b**) S2; (**c**) S3; (**d**) S4; (**e**) S5; (**f**) S6.