## 2 Results of Vocal-Emotion-Recognition Experiments  
### Speech Data  
The results of vocal-emotion-recognition experiments were collected from a previous study [17]. [PERSON] et al. used the Fujitsu Japanese Emotional Speech Database as the original speech data. In this database, a professional actress's sentences are expressed, and each sentence contains one of five emotions (neutral, joy, cold anger, sadness, and hot anger). In these experiments, the experimental stimuli of NVS were in three environments: noisy, reverberant, and noisy reverberant. The experimental stimuli were created with the following procedure.  
To produce noisy speech, [PERSON] et al. used stationary noise (white Gaussian noise), and the adjusted noise was added to the speech so that the SNRs of the original speech and noise would differ. As the noise conditions, SNRs of \(\infty\), \(20,15,10,5,0\), and \(-5\) dB were selected as the noise conditions, and SNR = \(\infty\) means a clean environment without noise. Therefore, there were a total of seven noise conditions.  
To produce reverberant speech, they used a statistical room-impulse response (Schroeder model) [18] and convoluted five types of room-impulse responses with \(T_{\text{R}}\) of \(0.1,0.2,0.5,1.0\), and \(2.0\) s into the original speech to make experimental stimuli in the reverberant environment. Since a no-reverberation environment, i.e., \(T_{\text{R}}\) = 0 s, was added, there were a total of six reverberation conditions.  
For the noisy reverberant environment, a reverberant speech was created by convolving three types of room-impulse responses with \(T_{\text{R}}\) of 0.5, 1.0, and 2.0 s into the original speech. Five types of constant noise (white Gaussian noise) with SNRs of 20, 10, 5, 0, and \(-5\) dB were then added to the reverberant speech. From the combination of the above cases, there were a total of 15 reverberation conditions.  
After adding noise and reverberation, the experimental stimuli of NVS were created. NVS is speech obtained by driving TAE information using band-limited random noise as a carrier signal [15; 17; 19]. NVS is primarily used to simulate the perception of speech as heard from CIs for normal-hearing listeners. Even in daily noise reverberation environments (SNR is greater than 10 dB and \(T_{\text{R}}\) is less than 1.0 s), the impact of these types of disturbances on experimental emotional perception using NVS was not significant [17]. Figure 1 shows the generation of the NVS stimuli. As the input signal, noisy reverberant emotional speech was divided into several frequency bands by using an auditory filterbank that simulates human frequency selectivity. The 6 th-order Butterworth infinite impulse response (IIR) band-pass filters (BPFs) were used as the auditory filterbank. The bandwidth of each filter was that of the human auditory filter, and the order of the filters was determined in accordance with the equivalent rectangular bandwidth (ERB\({}_{\text{N}}\)) and ERB\({}_{\text{N}}\)-number scale [20]. The unit of ERB\({}_{\text{N}}\)-number is Cam. The relationship between ERB\({}_{\text{N}}\)-number and acoustic frequency is defined as  
\[\text{ERB}_{\text{N}}-\text{number}=21.4\text{log}_{10}\left(\frac{4.37f}{100 0}+1\right), \tag{1}\]  
where \(f\) is the frequency in Hz, and subscript N indicates the characteristics of normal hearing. The signal was then constructed at the boundary frequencies of the BPFs, which were defined as an ERB\({}_{\text{N}}\)-number from 3 to 35 Cam with bandwidths of 2 ERB\({}_{\text{N}}\), and the number of channels was 16.  
In each frequency band, the TAE of the signal was extracted using the Hilbert transform and a 2 nd-order Butterworth IIR low-pass filter (LPF) with a cut-off frequency of 64 Hz.  
The TAE in each channel was then served with the band-limited noise generated by band-pass filtering white Gaussian noise at the same boundary frequency. All amplitude-modulated noise was summed to generate the NVS stimulus. The sampling frequency for stimulus creation was unified at 20 kHz.