A Dialogical Emotion Decoder for Speech Emotion Recognition in Spoken Dialog
| Autor: | Sung-Lin Yeh, Chi-Chun Lee, Yun-Shao Lin |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
Speech recognition media_common.quotation_subject Dialogical self Inference 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Class (biology) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Conversation Dialog system Dialog box computer Utterance 0105 earth and related environmental sciences media_common |
| Zdroj: | ICASSP |
| Popis: | Developing a robust emotion speech recognition (SER) system for human dialog is important in advancing conversational agent design. In this paper, we proposed a novel inference algorithm, a dialogical emotion decoding (DED) algorithm, that treats a dialog as a sequence and consecutively decode the emotion states of each utterance over time with a given recognition engine. This decoder is trained by incorporating intra- and inter-speakers emotion influences within a conversation. Our approach achieves a 70.1% in four class emotion on the IEMOCAP database, which is 3% over the state-of-art model. The evaluation is further conducted on a multi-party interaction database, the MELD, which shows a similar effect. Our proposed DED is in essence a conversational emotion rescoring decoder that can also be flexibly combined with different SER engines. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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