Mispronunciation Detection in Children's Reading of Sentences

• Autor(a) principal: Proença, Jorge
• Data de Publicação: 2018
• Outros Autores: Lopes, Carla Alexandra, Tjalve, Michael, Stolcke, Andreas, Candeias, Sara, Perdigão, Fernando
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10400.8/3353
• topic: Speech analysis; Mispronunciation detection; Children’s reading; Automatic reading annotation

This work proposes an approach to automatically parse children’s reading of sentences by detecting word pronunciations and extra content, and to classify words as correctly or incorrectly pronounced. This approach can be directly helpful for automatic assessment of reading level or for automatic reading tutors, where a correct reading must be identified. We propose a first segmentation stage to locate candidate word pronunciations based on allowing repetitions and false starts of a word’s syllables. A decoding grammar based solely on syllables allows silence to appear during a word pronunciation. At a second stage, word candidates are classified as mispronounced or not. The feature that best classifies mispronunciations is found to be the log-likelihood ratio between a free phone loop and a word spotting model in the very close vicinity of the candidate segmentation. Additional features are combined in multi-feature models to further improve classification, including: normalizations of the log-likelihood ratio, derivations from phone likelihoods, and Levenshtein distances between the correct pronunciation and recognized phonemes through two phoneme recognition approaches. Results show that most extra events were detected (close to 2% word error rate achieved) and that using automatic segmentation for mispronunciation classification approaches the performance of manual segmentation. Although the log-likelihood ratio from a spotting approach is already a good metric to classify word pronunciations, the combination of additional features provides a relative reduction of the miss rate of 18% (from 34.03% to 27.79% using manual segmentation and from 35.58% to 29.35% using automatic segmentation, at constant 5% false alarm rate).