Speech emotions are crucial in human communication and are extensively used in fields like speech synthesis and natural language understanding. Most prior studies, such as speech emotion recognition, have categorized speech emotions into a fixed set of classes. Yet, emotions expressed in human speech are often complex, and categorizing them into predefined groups can be insufficient to adequately represent speech emotions. On the contrary, describing speech emotions directly by means of natural language may be a more effective approach. Regrettably, there are not many studies available that have focused on this direction. Therefore, this paper proposes a speech emotion captioning framework named SECap, aiming at effectively describing speech emotions using natural language. Owing to the impressive capabilities of large language models in language comprehension and text generation, SECap employs LLaMA as the text decoder to allow the production of coherent speech emotion captions. In addition, SECap leverages HuBERT as the audio encoder to extract general speech features and Q-Former as the Bridge-Net to provide LLaMA with emotion-related speech features. To accomplish this, Q-Former utilizes mutual information learning to disentangle emotion-related speech features and speech contents, while implementing contrastive learning to extract more emotion-related speech features. The results of objective and subjective evaluations demonstrate that: 1) the SECap framework outperforms the HTSAT-BART baseline in all objective evaluations; 2) SECap can generate high-quality speech emotion captions that attain performance on par with human annotators in subjective mean opinion score tests.

翻译：语音情感在人类交流中至关重要，并在语音合成和自然语言理解等领域得到广泛应用。以往的研究（如语音情感识别）大多将语音情感归类为固定类别集合。然而，人类语音中表达的情感往往具有复杂性，对其进行预定义分类可能不足以充分表征语音情感。相比之下，通过自然语言直接描述语音情感或许是一种更有效的方法。遗憾的是，目前针对这一方向的研究相对较少。因此，本文提出了一种名为SECap的语音情感描述生成框架，旨在利用自然语言有效描述语音情感。得益于大语言模型在语言理解和文本生成方面的卓越能力，SECap采用LLaMA作为文本解码器以生成连贯的语音情感描述。此外，SECap利用HuBERT作为音频编码器提取通用语音特征，并采用Q-Former作为桥接网络向LLaMA提供与情感相关的语音特征。为实现这一目标，Q-Former通过互信息学习解耦情感相关语音特征与语音内容，同时采用对比学习提取更多情感相关语音特征。客观与主观评估结果表明：1) SECap框架在所有客观评估中均优于HTSAT-BART基线模型；2) SECap能够生成高质量的语音情感描述，在主观平均意见得分测试中达到与人工标注者相当的性能。