In Speech Emotion Recognition (SER), textual data is often used alongside
audio signals to address their inherent variability. However, the reliance on
human annotated text in most research hinders the development of practical SER
systems. To overcome this challenge, we investigate how Automatic Speech
Recognition (ASR) performs on emotional speech by analyzing the ASR performance
on emotion corpora and examining the distribution of word errors and confidence
scores in ASR transcripts to gain insight into how emotion affects ASR. We
utilize four ASR systems, namely Kaldi ASR, wav2vec, Conformer, and Whisper,
and three corpora: IEMOCAP, MOSI, and MELD to ensure generalizability.
Additionally, we conduct text-based SER on ASR transcripts with increasing word
error rates to investigate how ASR affects SER. The objective of this study is
to uncover the relationship and mutual impact of ASR and SER, in order to
facilitate ASR adaptation to emotional speech and the use of SER in real world.

研究利用自动语音识别系统分析情感语音，以及在转录情感语音时出现的错误，探讨自动语音识别对情感语音的适应性，进而促进情感语音的实际应用。