Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/5355
Autoria: | Medeiros, Henrique Moniz, Helena Batista, Fernando Trancoso, Isabel Nunes, Luís |
Data: | 30-Jul-2013 |
Título próprio: | Comparing different machine learning approaches for disfluency structure detection in a corpus of university lectures |
Título do evento: | Speech and Language Technology in Education (SLaTE 2013) |
Palavras-chave: | Machine learning speech processing prosodic features automatic detection of disfluencies |
Resumo: | machine learning methods on the identification of disfluencies and their distinct structural regions over speech data. Several machine learning methods have been applied, namely Naive Bayes, Logistic Regression, Classification and Regression Trees (CARTs), J48 and Multilayer Perceptron. Our experiments show that CARTs outperform the other methods on the identification of the distinct structural disfluent regions. Reported experiments are based on audio segmentation and prosodic features, calculated from a corpus of university lectures in European Portuguese, containing about 32h of speech and about 7.7% of disfluencies. The set of features automatically extracted from the forced alignment corpus proved to be discriminant of the regions contained in the production of a disfluency. This work shows that using fully automatic prosodic features, disfluency structural regions can be reliably identified using CARTs, where the best results achieved correspond to 81.5% precision, 27.6% recall, and 41.2% F-measure. The best results concern the detection of the interregnum, followed by the detection of the interruption point. |
Arbitragem científica: | Sim |
Acesso: | Acesso Restrito |
Aparece nas coleções: | CTI-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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comparing-ml-disfs - camera ready.pdf Restricted Access | 425,65 kB | Adobe PDF | Ver/Abrir Request a copy |
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