Catalan Accent Classification by Voice using Deep Learning
Speech characterization is a vital field in artificial intelligence, yet accent classification is often overlooked, particularly for the Catalan language. This project is centered on the classification of Catalan accents using the Catalan Common Voice dataset. We lay significant emphasis on our data...
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| Tipo de recurso: | tesis de maestría |
| Fecha de publicación: | 2023 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/399370 |
| Acceso en línea: | https://hdl.handle.net/2117/399370 |
| Access Level: | acceso abierto |
| Palabra clave: | Machine learning Artificial intelligence Automatic speech recognition artificial intelligence deep learning automatic speech recognition accent classification inteligencia artificial aprendizaje profundo reconocimiento automático del habla clasificación de acento Aprenentatge automàtic Intel·ligència artificial Reconeixement automàtic de la parla Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic |
| Sumario: | Speech characterization is a vital field in artificial intelligence, yet accent classification is often overlooked, particularly for the Catalan language. This project is centered on the classification of Catalan accents using the Catalan Common Voice dataset. We lay significant emphasis on our data processing pipeline, striving to ensure the quality and accuracy of the dataset for both training and validation phases. A novel aspect of our approach is the application of Double Multi-Head Self-Attention Pooling, diverging from the traditional statistical pooling methods typically employed in this task. This methodology enables effective pooling and dimensionality reduction of the feature vector, thereby boosting the efficiency of our models. We conduct various experiments to explore the optimal utilization of available data and to fine-tune our model for improved results. |
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