Emotion recognition of conversational affective speech using temporal course modeling

Jen Chun Lin, Chung Hsien Wu, Wen Li Wei

研究成果: Conference article同行評審

9 引文 斯高帕斯(Scopus)

摘要

In a natural conversation, a complete emotional expression is typically composed of a complex temporal course representing temporal phases of onset, apex, and offset. In this study, subemotional states are defined to model the temporal course of an emotional expression in natural conversation. Hidden Markov Models (HMMs) are adopted to characterize the subemotional states; each represents one temporal phase. A subemotion language model, which considers the temporal transition between sub-emotional states (HMMs), is further constructed to provide a constraint on allowable temporal structures to determine an optimal emotional state. Experimental results show that the proposed approach yielded satisfactory results on the MHMC conversation-based affective speech corpus, and confirmed that considering the complex temporal structure in natural conversation is useful for improving the emotion recognition performance from speech.

原文English
頁(從 - 到)1336-1340
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
出版狀態Published - 2013 1月 1
事件14th Annual Conference of the International Speech Communication Association, INTERSPEECH 2013 - Lyon, France
持續時間: 2013 8月 252013 8月 29

All Science Journal Classification (ASJC) codes

  • 語言與語言學
  • 人機介面
  • 訊號處理
  • 軟體
  • 建模與模擬

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