## 5 Results
In each sub-table, the rows represent the speech stimuli of the five categories of emotional speech, the columns represent the vocal-emotion-recognition results of the participants, and the values are the ratio of the number of times the subjects chose this emotion as the answer to the total answers (in percent). The diagonal values are the correct recognition rates for the emotions, and the other values are the percentages of these emotions misrecognized as other emotions. The higher the correct recognition rate, the easier it is to recognize this emotion. In Table 2, the rows represent the same four environments as in Table 1, and the columns represent the five categories of emotional speech, and each value represents the discrimination index of that emotion in that environment. From the equation of discrimination index in Section 3, the higher the discrimination index value, the easier to distinguish this emotion from all emotion categories. As Table 1 shows, in almost all environments, sad speech had higher correct recognition rates than other emotional speech, which suggests that sadness may be easier to distinguish from other emotions. The same trend is shown in Table 2: in almost all environments, sad speech had a higher discriminability index than other emotional speech. The misrecognition rates of joy and hot anger speech are higher in Table 1, which suggests that these two emotion categories are easily confused by the listener. Neutral speech had lower correct recognition rates, and the high misrecognition rates might be due to the experimental procedure. When participants were confused about the emotion of the experiment stimuli, most chose neutral emotion as the answer. The analysis of the vocal-emotion-recognition results and modulation spectrogram show a consistent trend. Both evinced that the vocal emotion recognition of sadness is easier than that of other emotion categories. However, we need more quantitative evidence to rigorously evaluate the above results. Therefore, to deeply investigate how MSFs contribute to the perception of vocal emotion recognition, we extracted the MSFs from the modulation spectrograms then calculated the similarity value between MSFs and the vocal-emotion-recognition results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Clean** & **Neutral** & **Joy** & **Cold Anger** & **Sadness** & **Hot Anger** \\ \hline
**Neutral** & 62.27 & 3.65 & 20.00 & 6.63 & 2.73 \\
**Joy** & 22.73 & 21.82 & 18.18 & 1.18 & 35.45 \\
**Cold anger** & 33.64 & 1.82 & 40.00 & 20.00 & 4.55 \\
**Sadness** & 4.55 & 0.00 & 5.46 & 90.00 & 0.00 \\
**Hot anger** & 16.36 & 2.73 & 5.46 & 0.91 & 74.55 \\ \hline
**SNR = 5 dB** & **Neutral** & **Joy** & **Cold Anger** & **Sadness** & **Hot Anger** \\ \hline
**Neutral** & 96.00 & 0.00 & 2.00 & 2.00 & 0.00 \\
**Joy** & 4.00 & 88.00 & 0.00 & 2.00 & 6.00 \\
**Cold anger** & 24.00 & 0.00 & 58.00 & 18.00 & 0.00 \\
**Sadness** & 0.00 & 0.00 & 2.00 & 98.00 & 0.00 \\
**Hot anger** & 6.00 & 10.00 & 12.00 & 0.00 & 72.00 \\ \hline \hline
**\(T_{\text{R}}\) = 1.0 s** & **Neutral** & **Joy** & **Cold Anger** & **Sadness** & **Hot Anger** \\ \hline
**Neutral** & 86.96 & 0 & 4.35 & 8.70 & 0.00 \\
**Joy** & 0.00 & 93.33 & 0.00 & 2.22 & 4.44 \\
**Cold anger** & 30.23 & 0.00 & 44.19 & 25.58 & 0.00 \\
**Sadness** & 11.11 & 0.00 & 4.44 & 84.44 & 0.00 \\
**Hot anger** & 17.78 & 6.67 & 0.00 & 0.00 & 75.56 \\ \hline
**SNR = 10 dB** & **Neutral** & **Joy** & **Cold Anger** & **Sadness** & **Hot Anger** \\ \hline
**Neutral** & 90.00 & 0.00 & 6.00 & 4.00 & 0.00 \\
**Joy** & 8.00 & 80.00 & 4.00 & 2.00 & 6.00 \\
**Cold anger** & 26.00 & 0.00 & 58.00 & 16.00 & 0.00 \\
**Sadness** & 4.00 & 0.00 & 4.00 & 92.00 & 0.00 \\
**Hot anger** & 14.00 & 14.00 & 10.00 & 0.00 & 62.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean confusion matrix of vocal-emotion-recognition results (in percent). The confusion matrix is presented as a percentage with stimuli organized vertically and response categories organized horizontally.