2026-06-03T01:32:45Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/331132026-02-03T11:14:12Zcom_10630_2254col_10630_37953

Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically? Moreno-Torres-Sánchez, Ignacio Lozano, Andrés Nava-Baro, Enrique Bermúdez-de-Alvear, Rosa María Hipernasalidad Automatic tools to detect hypernasality have been traditionally designed to analyze sustained vowels exclusively. This is in sharp contrast with clinical recommendations, which consider it necessary to use a variety of utterance types (e.g., repeated syllables, sustained sounds, sentences, etc.) This study explores the feasibility of detecting hypernasality automatically based on speech samples other than sustained vowels. The participants were 39 patients and 39 healthy controls. Six types of utterances were used: counting 1-to-10 and repetition of syllable sequences, sustained consonants, sustained vowel, words and sentences. The recordings were obtained, with the help of a mobile app, from Spain, Chile and Ecuador. Multiple acoustic features were computed from each utterance (e.g., MFCC, formant frequency) After a selection process, the best 20 features served to train different classification algorithms. Accuracy was the highest with syllable sequences and also with some words and sentences. Accuracy increased slightly by training the classifiers with between two and three utterances. However, the best results were obtained by combining the results of multiple classifiers. We conclude that protocols for automatic evaluation of hypernasality should include a variety of utterance types. It seems feasible to detect hypernasality automatically with mobile devices. 2024-09-24T17:33:16Z 2024-09-24T17:33:16Z 2024-09 2021-09-11 journal article Moreno-Torres, I.; Lozano, A.; Nava, E.; Bermúdez-de-Alvear, R. Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically? Appl. Sci. 2021, 11, 8809. https://doi.org/10.3390/ app11198809 https://hdl.handle.net/10630/33113 10.3390/app11198809 eng open access MDPI