Which Utterance Types Are Most Suitable to Detect Hypernasality Automatically?

Applied Sciences

Ignacio Moreno-Torres¹  Andrés Lozano²  Enrique Nava²  Rosa Bermúdez-de-Alvear³ 

[1] Departamento de Filología Española, Universidad de Málaga, 29071 Málaga, Spain;Departamento de Ingeniería de Comunicaciones, Universidad de Málaga, 29071 Málaga, Spain;Departamento de Personalidad, Evaluación y Tratamiento Psicológico, Universidad de Málaga, 29071 Málaga, Spain;

关键词: hypernasality;    Spanish language;    speech acoustic features;    ANN;    automatic detection of speech deficits;   

DOI  :  10.3390/app11198809

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown