The prevalent approach in speech emotion recognition (SER) involves integrating both audio and textual information to comprehensively identify the speaker's emotion, with the text generally obtained through automatic speech recognition (ASR). An essential issue of this approach is that ASR errors from the text modality can worsen the performance of SER. Previous studies have proposed using an auxiliary ASR error detection task to adaptively assign weights of each word in ASR hypotheses. However, this approach has limited improvement potential because it does not address the coherence of semantic information in the text. Additionally, the inherent heterogeneity of different modalities leads to distribution gaps between their representations, making their fusion challenging. Therefore, in this paper, we incorporate two auxiliary tasks, ASR error detection (AED) and ASR error correction (AEC), to enhance the semantic coherence of ASR text, and further introduce a novel multi-modal fusion (MF) method to learn shared representations across modalities. We refer to our method as MF-AED-AEC. Experimental results indicate that MF-AED-AEC significantly outperforms the baseline model by a margin of 4.1\%.

翻译：当前语音情感识别的主流方法通过融合音频与文本信息来全面识别说话者的情感，其中文本通常经由自动语音识别系统获取。该方法的一个关键问题在于，文本模态中的ASR错误会降低语音情感识别的性能。先前研究提出采用辅助性的ASR错误检测任务，以自适应地为ASR假设中的每个词语分配权重。然而，由于该方法未解决文本语义信息的一致性问题，其性能提升存在局限。此外，不同模态固有的异质性导致其表征之间存在分布差异，使得多模态融合面临挑战。为此，本文引入ASR错误检测与ASR错误纠正两项辅助任务，以增强ASR文本的语义连贯性，并进一步提出一种新颖的多模态融合方法以学习跨模态的共享表征。我们将该方法命名为MF-AED-AEC。实验结果表明，MF-AED-AEC以4.1%的显著优势超越基线模型。