Analysis of Deep Learning-Based Decision-Making in an Emotional Spontaneous Speech Task
In this work, we present an approach to understand the computational methods and decision-making involved in the identification of emotions in spontaneous speech. The selected task consists of Spanish TV debates, which entail a high level of complexity as well as additional subjectivity in the human...
| Autores: | , , , |
|---|---|
| Tipo de documento: | artigo |
| Data de publicação: | 2023 |
| País: | España |
| Recursos: | Universidad del País Vasco |
| Repositório: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/59420 |
| Acesso em linha: | http://hdl.handle.net/10810/59420 |
| Access Level: | Acceso aberto |
| Palavra-chave: | emotion detection speech processing explainable artificial intelligence machine learning |
| Resumo: | In this work, we present an approach to understand the computational methods and decision-making involved in the identification of emotions in spontaneous speech. The selected task consists of Spanish TV debates, which entail a high level of complexity as well as additional subjectivity in the human perception-based annotation procedure. A simple convolutional neural model is proposed, and its behaviour is analysed to explain its decision-making. The proposed model slightly outperforms commonly used CNN architectures such as VGG16, while being much lighter. Internal layer-by-layer transformations of the input spectrogram are visualised and analysed. Finally, a class model visualisation is proposed as a simple interpretation approach whose usefulness is assessed in the work. |
|---|