语音声学上下文与情感识别之间关系的实证解释
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition
AI总结：
本研究使用基于注意力的方法探讨声学上下文和音素边界对语音情感识别局部标记的影响，通过跨语料库实验验证分布式情感理解的优势，旨在连接心理语言学理论与计算建模。
摘要：
语音情感识别（SER）对于获得情感智能和理解语音的上下文意义至关重要。辅音-元音（CV）音素边界的变化可以通过语言线索丰富声学上下文，从而影响SER。在实际应用中，语音情感被视为给定时间段内声学片段上的单一标签。然而，语音内的音素边界并非离散事件，因此感知到的情感状态也应分布在潜在的连续时间窗口上。本研究使用基于注意力的方法探讨了声学上下文和音素边界对SER局部标记的影响。跨语料库分析实验的结果支持了使用分布式方法进行语音情感理解的优势。实验将音素和单词映射到注意力向量上，并结合基频观察重叠分布，从而揭示声学上下文与情感之间的关系。本研究旨在将心理语言学理论研究与SER的计算建模联系起来。
英文摘要：
Speech emotion recognition (SER) is vital for obtaining emotional intelligence and understanding the contextual meaning of speech. Variations of consonant-vowel (CV) phonemic boundaries can enrich acoustic context with linguistic cues, which impacts SER. In practice, speech emotions are treated as single labels over an acoustic segment for a given time duration. However, phone boundaries within speech are not discrete events, therefore the perceived emotion state should also be distributed over potentially continuous time-windows. This research explores the implication of acoustic context and phone boundaries on local markers for SER using an attention-based approach. The benefits of using a distributed approach to speech emotion understanding are supported by the results of cross-corpora analysis experiments. Experiments where phones and words are mapped to the attention vectors along with the fundamental frequency to observe the overlapping distributions and thereby the relationship between acoustic context and emotion. This work aims to bridge psycholinguistic theory research with computational modelling for SER.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
