Acoustic Speech Analysis for Hypernasality Detection in Children

Castellanos, Germán; Daza, Gabriel Monzón; Robayo-Sánchez, Laura Natalia; Castrillón, Omar Danilo; Suárez, Jorge

doi:10.1109/iembs.2006.4398702

Cited by 3 publications

(3 citation statements)

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“…For speech signals, the spectrum is composed by periodic sources such as vowels and nasal sounds, and noise sources such as unvoiced and fricative sounds (/f/, /th/, /s/, /sh/) [7].…”

Section: Linear Predictive Coding (Lpc)mentioning

confidence: 99%

Pattern recognition of hypernasality in voice of patients with Cleft and Lip Palate

Nieto

Marin-Hurtado

Capacho-Valbuena

et al. 2014

2014 XIX Symposium on Image, Signal Processing and Artificial Vision

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The Cleft and Lip Palate (CLP) is a malformation with high recurrence in Colombia, which affects the ability of the phonation system, making difficult the effective communication of the patient. This research seeks to find patterns that enable to detect hypernasality without using invasive diagnostic methods. We performed an analysis of a large range of acoustic features to identify those capable of discriminating hypernasality. The analyzed features include: Teager energy operator (TEO), linear predictive coding (LPC), Mel Frequency Cepstral Coefficients (MFCC), Pitch, Jitter, Shimmer, and the first three formants together with the bandwidth of the first formant. With the correct configuration is achieved discriminant patterns classify 99 percent of patients hypernasal of the database with a false positive rate of less than 1 percent of healthy patients, which are promising results as a starting point for creating a tool for automatic noninvasive detection of hypernasality.

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“…For speech signals, the spectrum is composed by periodic sources such as vowels and nasal sounds, and noise sources such as unvoiced and fricative sounds (/f/, /th/, /s/, /sh/) [7].…”

Section: Linear Predictive Coding (Lpc)mentioning

confidence: 99%

Pattern recognition of hypernasality in voice of patients with Cleft and Lip Palate

Nieto

Marin-Hurtado

Capacho-Valbuena

et al. 2014

2014 XIX Symposium on Image, Signal Processing and Artificial Vision

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“…Debido a que el 90% de los pacientes con LPH son hipernasales, es de especial interés científi-co estudiar esta patología (Castellanos, et al 2006). Desde la dé-cada de 1970, existen estudios sobre análisis acústico de voces patológicas (Fujimura & Lindqvist, 1971).…”

Section: Introductionunclassified

Operador de energía de teager para la detección de hipernasalidad en niños con labio y paladar hendido

Orozco-Arroyave

Uribe

Vargas-Bonilla

2011

TecnoL.

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ResumenEl labio y/o paladar hendido (LPH) es una malformación, que tiene orígenes de tipo genético y ambiental. En Colombia, 6 de cada 10000 niños nacen con esta malformación, mientras en el resto del mundo la proporción se encuentra en 1 de cada 10000. El LPH trae consigo patologías en el habla tales como: hipernasalidad, hiponasalidad, golpe glótico, entre otras. De todas estas patologías, la hipernasalidad es la más recurrente en pacientes con LPH, apareciendo aproximadamente en el 90% de los casos. En este trabajo se hace un análisis, basado en resultados experimentales, del desempeño del Operador de Energía de Teager (TEO, por las siglas en inglés de Teager Energy Operator), para la detección de hipernasalidad en pacientes con LPH. Se analiza una versión generalizada del TEO con el fin de validar su capacidad discriminante en la detección de hipernasalidad, aplicándolo sobre una base de datos con registros de voz reales, de niños con LPH y niños control. Los resultados obtenidos comprueban que el TEO posee gran capacidad discriminante, y puede aportar información relevante en el proceso de detección de hipernasalidad.

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“…And the main methods of hypernasality detection are divided into three categories using: 1) acoustic parameters; 2) vocal tract model; 3) nasal formant. Castellanos et al use a set of parameters including pitch, jitter, tone perturbation coefficient, energy, zero crossings, LPC and MFCC to detect hypernasality [6]. Ehsan et al put forward an ARMA model with typical number of poles and appropriate number of zeros to model the hypernasal speech signal.…”

Section: Introductionmentioning

confidence: 99%

The Automatic Detection of Hypernasality in Cleft Palate Speech Based on an Improved Cepstrum Method

Fu¹,

Qian²,

He³

et al. 2017

Proceedings of the 3rd Annual International Conference on Electronics, Electrical Engineering and Information Science (EEEIS 20

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Abstract. Cleft palate is a birth defect with the resonance between the nasal cavity and oral cavity, which causes an additional nasal formant. And the accurate formant extraction method contributes to hypernasality detection. In this sense, we present a method for formant extraction. The proposed approach uses multi-resolution analysis of automatic segmented filtering combining cepstrum method to accurately extract the formants. It can increase the resolution in the low-frequency region, which is helpful for extracting the addition nasal formant around 250Hz. The proposed technique is tested on 416 vowels /a/ recorded by the West China Hospital of Stomatology, including 216 vowels for normal speech and 210 vowels for cleft palate speech. The formant frequency and formant amplitude are used as acoustic features for hypernasality detection, and the detection accuracy of hypernasality in cleft palate speech is 97.41%.

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Acoustic Speech Analysis for Hypernasality Detection in Children

Cited by 3 publications

References 0 publications

Pattern recognition of hypernasality in voice of patients with Cleft and Lip Palate

Pattern recognition of hypernasality in voice of patients with Cleft and Lip Palate

Operador de energía de teager para la detección de hipernasalidad en niños con labio y paladar hendido

The Automatic Detection of Hypernasality in Cleft Palate Speech Based on an Improved Cepstrum Method

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