## 4 Discussion
The noise datasets CHIME [26] and MS-SNSD [27] were used for training and validation, and NoiseX92 [28] was used for test. REAPER [24] was used to extract the pitch mark timing from the clean speech; the pitch mark timing data were used to generate the ground-truth pitch-related waveform. The accuracy degradation for a noisy speech input waveform with the 0 dB SNR is 3.8% in this work while it is larger than 15% in the published SER works for the noisy environments [7; 17; 18; 19]. It is estimated that the use of the four band-pass filtered waveforms of a noisy speech as an input to the model contributes to the robust operation of this work under noisy environments. The four band-pass filtered waveforms retain the entire information of the noisy speech input without any loss of information. Because the published SER works for noisy environments use simplified input features such as MFCC as input to the model, it is conjectured that a significant amount of information may be lost during the simplification process of the input features. The accuracy degrades by 5.2% and 7.2% at SNRs of \(-\)3 dB and \(-\)6 dB, respectively, compared to the clean speech case. If the four band-pass filtered waveforms are replaced by the raw speech waveform in this work, the accuracy averaged over different SNR values down to \(-\)6 dB degrades by 4.3% compared to the clean speech case. If the pitch-related waveform is further removed in the above case, the averaged accuracy degrades by 5.7%. The ablation study indicates that the 0\(\sim\)1 kHz waveform is the most important (accuracy degradation of 4.9% compared to the proposed model), and the 1 kHz\(\sim\)2 kHz waveform is the second most important (4.0%). The high-frequency waveforms (2 kHz\(\sim\)4 kHz and 4 kHz\(\sim\)8 kHz) cannot be neglected in the emotion recognition (2.2% and 2.5%, respectively). The pitch-related waveform is also helpful to enhance the accuracy (1.9%). The model trained with a combined dataset (IEMOCAP + ESD) is resilient to the overfitting problem compared to the models trained with a single dataset (IEMOCAP or ESD). All the SER datasets [25; 29; 30; 31] used in this work are recorded by actors, not by ordinary people. Because the emotions from actors may be over-emphasized compared to the emotions from ordinary people in real life, the models trained with the datasets from actors might suffer from accuracy degradation in real-life applications. Although the SER datasets recorded by ordinary people in real life are not available nowadays except for the video challenge [32] to the best of the authors' knowledge, it is considered desirable to use the SER datasets recorded by ordinary people in real life for future works. Conceptualization, [PERSON] and [PERSON]; software, [PERSON]; project administration, [PERSON]; [PERSON] and [PERSON] worked together during the whole editorial process of the manuscript. All authors were involved in the preparation of this manuscript. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2022R1A2C2003451).