Caracterització de locutors usant deep learning

Desenvolupament d'un sistema computacional capaç de comparar dos fitxers d'àudio, on parla una persona, i determinar si comparteixen locutor, amb un error òptim del 29,77%; i d'una interfície gràfica que l'utilitza.Development of a computer system capable of comparing two audio files, where one person talks, and determine if these sequences share speakers, with an optimal error rate of 29,77%; and a graphics interface that uses it

Características acústicas de patologias vocais no português europeu

Mestrado em Ciências da fala e da audiçãoObjetivos: O estudo teve como principal objetivo comparar acusticamente as vozes de doentes com Refluxo Laringofaríngeo, Edema de Reinke, Nódulos, Pólipos, Quistos e Paralisia Unilateral da Prega Vocal por Lesão do Nervo Laríngeo Recorrente. Os parâmetros acústicos utilizados foram Shimmer (apq11), Jitter (ppq5), HNR (dB) e Média, Mediana e Desvio-padrão da Frequência Fundamental (F0). Também se estudou a relação entre os seis grupos de patologias vocais e os dados clínicos e demográficos dos doentes (hábitos tabágicos, faixa etária, índice de massa corporal, uso da voz no canto e sexo). Métodos: A amostra estudada foi constituída por 233 doentes com patologia vocal. Para cada um deles foi gravada e analisada, acusticamente, a produção da vogal /a/ sustentada e foram anotados os seus dados clínicos e demográficos. Resultados: Relativamente às comparações entre patologias, os resultados indicaram diferenças significativas para os parâmetros Shimmer (p=0,005), Jitter (p=0,031), Média de F0 do sexo feminino (p<0,001) e Mediana de F0 do sexo feminino (p<0,001). Quanto às relações entre as patologias e os dados clínicos dos doentes, obtiveram-se resultados estatisticamente significativos entre as patologias vocais e os hábitos tabágicos (p<0,001), a faixa etária (p<0,001) e o sexo dos doentes (p=0,043). Conclusões: Os resultados do estudo permitiram concluir que os parâmetros Shimmer, Jitter, HNR, Média de F0 e Mediana de F0 possibilitam a distinção entre algumas das patologias vocais estudadas. Também se concluiu que existiu uma relação estatisticamente significativa de grau fraco, fraco a moderado e moderado, entre as patologias vocais e o sexo, a faixa etária e os hábitos tabágicos dos doentes, respetivamente.Objectives: The present study aimed to acoustically compare patients’ voices with Laryngophanyngeal Reflux, Reinke’s Edema, Vocal Fold Nodules, Vocal Fold Polyps, Vocal Fold Cyst and Unilateral Recurrent Laryngeal Nerve Pathology. The comparison was conducted through acoustic parameters Shimmer (apq11), Jitter (ppq5), HNR (dB) as well as Mean, Median and Standard Deviation of the Fundamental Frequency (F0). An additional analysis of the relation between voice disorders groups and patients’ clinical and demographic data was considered (smoking habits, age group, body mass index, gender and use of singing voice). Methodology: The sample consisted of 233 patients with voice disorders. For each participant, their voice was recorded and analyzed acoustically through the production of the sustained vowel /a/ and their clinical and demographic data was registered. Results: Results indicated a significant difference between voice disorders for parameters Shimmer (p=0,005), Jitter (p=0,031), F0 mean for women (p<0,001) and F0 median for women (p<0,001). The relations between variables showed statistical differences between voice disorders and smoking habits (p<0,001), age group (p<0,001) and gender (p=0,043). Conclusions: This study concludes that the parameters Shimmer, Jitter, F0 mean and F0 median allow a differentiation between certain voice disorders. Additionally, significant weak, weak to moderate and moderate relations were evidenced between voice disorders and gender, age group and smoking habits, respectively

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders provides a survey of methods designed to aid clinicians in the diagnosis and monitoring of speech disorders such as dysarthria and dyspraxia, with an emphasis on the signal processing techniques, statistical validity of the results presented in the literature, and the appropriateness of methods that do not require specialized equipment, rigorously controlled recording procedures or highly skilled personnel to interpret results. Such techniques offer the promise of a simple and cost-effective, yet objective, assessment of a range of medical conditions, which would be of great value to clinicians. The ideal scenario would begin with the collection of examples of the clients’ speech, either over the phone or using portable recording devices operated by non-specialist nursing staff. The recordings could then be analyzed initially to aid diagnosis of conditions, and subsequently to monitor the clients’ progress and response to treatment. The automation of this process would allow more frequent and regular assessments to be performed, as well as providing greater objectivity

Automatic Speech Signal Analysis for Clinical Diagnosis and Assessment of Speech Disorders

VIII, 70 p. 9 illus.online resource