## 3 Experimental Results  
In this work, the clean speech utterances were taken from the 7-h-long IEMOCAP [25] with 5531 utterances with four emotions (angry, happy, sad, and neutral) (Table 2); 4425 (80%) utterances were assigned for training, 555 (10%) utterances were assigned for validation, and 551 (10%) utterances were assigned for the test. The average and the standard deviation of the time duration of the IEMOCAP utterances are 4.5 s and 3.2 s, respectively. The time duration of the clean speech utterance was fixed at 7.5 s by taking only 7.5 s  
Figure 3: Generation of the ground-truth waveforms used for training. (**a**) ground-truth clean speech waveform, (**b**) ground-truth \(y_{1}\), (**c**) ground-truth \(y_{2}\), (**d**) ground-truth \(y_{4}\), (**e**) ground-truth \(y_{8}\), (**f**) ground-truth \(y_{p}\), the vertical red dotted lines are the pitch mark timings. portion of the IEMOCAP utterances longer than 7.5 s and zero-padding the IEMOCAP utterances shorter than 7.5 s.  
The noise datasets in CHIME3 [26] and MS-SNSD [27] were used for training and validation. The NoiseX92 [28] was used for the test. The data augmentation was used for training; a noisy speech waveform is generated by adding a clean speech utterance and randomly selected noise data with a random SNR among \(-6\), \(-3\), \(0\), \(3\), \(6\), and \(\infty\) (clean) dB, at every epoch. To enhance the reliability of the test accuracy, the 551 utterances assigned for the test were multiplied by 15 times to 8265 utterances; an utterance out of the original 551 utterances was added to all the 15 noise files of the NoiseX92 dataset to generate 15 noisy speech waveforms. The accuracy of this work was compared to the published SER works under noisy environments (Table 3). While the published SER works use simplified input features such as MFCC, WPCC, and a 6552 vector, this work uses the original speech waveform as input without any simplification process. It is estimated that the information loss of the simplified input features may be susceptible to accuracy degradation due to noise. The accuracy values of this work are 66.2% and 62.4% for SNRs of \(\infty\) (clean) and 0 dB, respectively; the accuracy degradation at an SNR of 0 dB is 3.8% in this work, while the accuracy degradations of other works at an SNR of 0 dB are 15.5% or larger. The accuracy degradations of this work at SNRs of \(-3\) dB and \(-6\) dB are 5.2% and 7.2%, respectively. The accuracy comparison of this work with [19] with SNR down to \(-6\) dB (Table 4) presents that the accuracy degradations of this work due to noise are 0.7%, 2.4%, and 3.8% at SNRs of 20 dB, 5 dB, and 0 dB, respectively, while those in [19] are 2.2%, 11.1%, and 15.5%, respectively. This demonstrates that this work is more resilient to noise than [19]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Params**} & \multicolumn{5}{c}{**Accuracy (\%)**} \\ \cline{3-7}  & & \(\infty\) & **20 dB** & **5 dB** & **0 dB** & \(-3\) dB & \(-6\) dB \\ \hline
[19] & 19.7 M & 62.7 & 60.5 & 51.6 & 47.2 & - & - \\ This work & 4.2 M & 66.2 & 65.5 & 63.8 & 62.4 & 61.0 & 59.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy comparison with SNR down to \(-6\) dB. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Subset**} & \multirow{2}{*}{**Usage**} & \multirow{2}{*}{
\begin{tabular}{c} **Time** \\ **(hours)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{Clean} & \multirow{2}{*}{IEMOCAP [25]} & \multicolumn{2}{c}{5531 utterances of four classes} & \multirow{2}{*}{tr, va, te} & \multirow{2}{*}{7.0} \\  & & (angry, happy, neutral, sad) & & & \\ \hline \multirow{3}{*}{Noise} & CHIME3 [26] & backgrounds & tr, va & 8.4 \\  & MS-SNSD [27] & noise\_train & tr & 2.8 \\  & Noise\_test & va & 0.5 \\ \cline{1-1}  & NoiseX92 [28] & - & te & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset used in this work, ‘tr’: training, ‘va’: validation, ‘te’: test.