Research on LSTM+Attention Model of Infant Cry Classification
According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used inste...
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| Autores principales: | , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Atlantis Press
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/79d57d420c7e4b93b6fe9f3e3217f915 |
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| Sumario: | According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. |
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