Detección automática de voz hipernasal de niños con labio y paladar hendido a partir de vocales y palabras del español usando medidas clásicas y análisis no lineal

RESUMEN: Este artículo presenta un sistema para la detección automática de señales de voz hipernasales basado en la combinación de dos diferentes esquemas de caracterización aplicados en las cinco vocales del español y dos palabras seleccionadas. El primer esquema está basado en características clásicas como perturbaciones del periodo fundamental, medidas de ruido y coeficientes cepstrales en la frecuencia de Mel. El segundo enfoque está basado en medidas de dinámica no lineal. Las características más relevantes son seleccionadas usando dos técnicas: análisis de componentes principales y selección flotante hacia adelante secuencial. La decisión acerca de si un registro de voz es hipernasal o sano es tomada usando una máquina de soporte vectorial de margen suave. Los experimentos consideran grabaciones de las cinco vocales del idioma español y las palabras y se consideran, asimismo, tres conjuntos de características: (1) el enfoque clásico, (2) el análisis de dinámica no lineal y (3) la combinación de ambos esquemas. En general, los aciertos son mayores y más estables cuando las características clásicas y no lineales son combinadas, indicando que el análisis de dinámica no lineal se complementa con el esquema clásico.ABSTRACT: This paper presents a system for the automatic detection of hypernasal speech signals based on the combination of two different characterization approaches applied to the five spanish vowels and two selected words. The first approach is based on classical features such as pitch period perturbations, noise measures, and Mel-Frequency Cepstral Coefficients (MFCC). The second approach is based on the Non-Linear Dynamics (NLD) analysis. The most relevant features are selected and sorted using two techniques: Principal Components Analysis (PCA) and Sequential Forward Floating Selection (SFFS). The decision about whether a voice record is hypernasal or healthy is taken using a Soft Margin - Support Vector Machine (SM-SVM). Experiments upon recordings of the five Spanish vowels and the words are performed considering three different set of features: (1) the classical approach, (2) the NLD analysis, and (3) the combination of the classical and NLD measures. In general, the accuracies are higher and more stable when the classical and NLD features are combined, indicating that the NLD analysis is complementary to the classical approach

Acoustic analysis of the unvoiced stop consonants for detecting hypernasal speech

Speakers having evidence of a defective velopharyngeal mechanism produce speech with inappropriate nasal resonance (hypernasal speech). Voice analysis methods for the detection of hypernasality commonly use vowels and nasalized vowels. However, to obtain a more general assessment of this abnormality it is necessary to analyze stops and fricatives. This study describes a method for hipernasality detection analyzing the unvoiced Spanish stop consonants /k/ and /p/, as well. The importance of phonemeby- phoneme analysis is shown, in contrast with whole word parametrization which may include irrelevant segments from the classification point of view. Parameters that correlate the imprints of Velopharyngeal Incompetence (VPI) over voiceless stop consonants were used in the feature estimation stage. Classification was carried out using a Support Vector Machine (SVM), obtaining a performance of 74% for a repeated cross-validation strategy evaluation

Hypernasal Speech Detection by Acoustic Analysis of Unvoiced Plosive Consonants

Las personas con un mecanismo velofaringeo defectuoso hablan con una resonancia nasal anormal (habla hipernasal). Métodos de análisis de voz para detección de hipernasaliad comúnmente usan las vocales y las vocales nasales. Sin embargo para obtener una evaluación más general de esta anormalidad es necesario analizar las paradas y las fricativas. Este estudio describe un método con alta capacidad de generalización para detección de hipernasalidad análisis de las consonantes oclusivas sordas españolas. Se muestra la importancia del análisis fonema por fonema, en contraste con la parametrización de la palabra completa que incluye segmentos irrelevantes desde el punto de vista de la clasificación. Los parámetros que correlacionan la incompetencia velofaringea (VPI) sobre las consonantes oclusivas sordas se usa en la fase de estimación de características. La clasificación se llevó a cabo usando una Maquina de Vector de Soporte (SVM), incluyendo el modelo de complejidad Rademacher con el objetivo de aumentar la capacidad de generalización. Rendimientos del 95.2% y del 92.7% fueron obtenidos en las etapas de elaboración y verificación para una repetida evaluación y clasificación de validación cruzada.People with a defective velopharyngeal mechanism speak with abnormal nasal resonance (hypernasal speech). Voice analysis methods for hypernasality detection commonly use vowels and nasalized vowels. However to obtain a more general assessment of this abnormality it is necessary to analyze stops and fricatives. This study describes a method with high generalization capability for hypernasality detection analyzing unvoiced Spanish stop consonants. The importance of phoneme-by-phoneme analysis is shown, in contrast with whole word parametrization which includes irrelevant segments from the classification point of view. Parameters that correlate the imprints of Velopharyngeal Incompetence (VPI) over voiceless stop consonants were used in the feature estimation stage. Classification was carried out using a Support Vector Machine (SVM), including the Rademacher complexity model with the aim of increasing the generalization capability. Performances of 95.2% and 92.7% were obtained in the processing and verification stages for a repeated cross-validation classifier evaluation

Hypernasal Speech Analysis via Emperical Mode Decomposition and the Teager-Kasiser Energy Operator

In the area of speech science, one particular problem of importance has been to develop a clear method for detecting hypernasality in speech. For speech pathologists, hypernsality is a critical diagnostic used for judging the severity of velopharyngeal (nasal cavity/mouth separation) inadequacy in children with a cleft lip or cleft palate condition. For physicians and particularly neurologists, these same velopharyngeal inadequacies are believed to be linked to nervous system disorders such as Alzheimers disease and particularly Parkinson\u27s disease. One can therefore envision the need to not only find a reliable method for detecting hypernasality, but to also quantify the level (severity) of hypernasality as well. An integral component in the study of speech is the analysis of speech formants, i.e., vocal tract resonances. Traditional acoustical analysis methods of using a linear source model follow the premise that differences between normal and hypernasal speech can be distinguished by shifts or power changes in the formant frequencies and/or the widening (or narrowing) of the formant bandwidths. Such a premise, however, has not been validated with consistency. Part of the reason is that traditional acoustical analysis methods such as one-third octave band, LPC (Linear Predictive Coding), and cepstral analysis are ill-equipped to deal with the nonlinear, non-stationary, and wideband characteristics of normal and nasal speech signals. Relatively newer DSP methods that employ group delay or energy separation overcome some of these problems, but have their own issues such as possible mode mixing, noise, and the aforementioned wideband problem. However, initial investigations into energy separation methods show promise as long as these issues can be resolved. This thesis evaluates the success of a novel acoustical energy approach which deals with the mode mixing and wideband problems where: (1) a DSP sifting algorithm known as the EMD (Empirical Mode Decomposition) is first implemented to decompose the voice signal into a number of IMFs (Intrinsic Mode Functions). (2) Energy analysis is performed on each IMF via the Teager-Kaiser Energy Operator. The proposed EMD energy approach is applied to voice samples taken from the American CLP Craniofacial database and is shown to produce a clear delineation between normal and nasal samples and between different levels of hypernasality.\u2

Early speech deterioration in amyotrophic lateral sclerosis: A case study of newscaster Donna Britt

Abstract Purpose: The aim of this study is to detect and track speech changes using acoustic and perceptual measures in an individual (a local newscaster) with amyotrophic lateral sclerosis (ALS) using data from before her diagnosis, around the time of her diagnosis, and after her diagnosis. Methods: Six time points, ranging from 37 months around her diagnosis, were analyzed from one speaker with ALS. Three acoustic parameters were measured: articulation rate, acoustic vowel space, and the slope of the second formant. Additionally, two experts with more than 10 years of experience perceptually analyzed the speech samples on 17 characteristics using a 7-point scale. Results: The findings of this study revealed a decline in most parameters, both acoustic and perceptual, in the last time point, as expected. Additionally, a gradual increase in articulation rate, acoustic vowel space, and the number of syllables per utterance is observed before a decline after time point 4. Discussion: The gradual increase in some parameters before a decline may reflect the speaker’s compensatory strategies. After time point 4, it is suspected the speaker could no longer compensate, and thus a decline is observed. Additionally, both perceptual and acoustic measures indicate speech changes over time; however, acoustic measures (i.e. articulation rate) appear to provide objective data to support subtle, perceptual changes between time points

Assessment of severe apnoea through voice analysis, automatic speech, and speaker recognition techniques

The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2009/1/982531This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.The activities described in this paper were funded by the Spanish Ministry of Science and Technology as part of the TEC2006-13170-C02-02 Project

Automatic Screening of Childhood Speech Sound Disorders and Detection of Associated Pronunciation Errors

Speech disorders in children can affect their fluency and intelligibility. Delay in their diagnosis and treatment increases the risk of social impairment and learning disabilities. With the significant shortage of Speech and Language Pathologists (SLPs), there is an increasing interest in Computer-Aided Speech Therapy tools with automatic detection and diagnosis capability. However, the scarcity and unreliable annotation of disordered child speech corpora along with the high acoustic variations in the child speech data has impeded the development of reliable automatic detection and diagnosis of childhood speech sound disorders. Therefore, this thesis investigates two types of detection systems that can be achieved with minimum dependency on annotated mispronounced speech data. First, a novel approach that adopts paralinguistic features which represent the prosodic, spectral, and voice quality characteristics of the speech was proposed to perform segment- and subject-level classification of Typically Developing (TD) and Speech Sound Disordered (SSD) child speech using a binary Support Vector Machine (SVM) classifier. As paralinguistic features are both language- and content-independent, they can be extracted from an unannotated speech signal. Second, a novel Mispronunciation Detection and Diagnosis (MDD) approach was introduced to detect the pronunciation errors made due to SSDs and provide low-level diagnostic information that can be used in constructing formative feedback and a detailed diagnostic report. Unlike existing MDD methods where detection and diagnosis are performed at the phoneme level, the proposed method achieved MDD at the speech attribute level, namely the manners and places of articulations. The speech attribute features describe the involved articulators and their interactions when making a speech sound allowing a low-level description of the pronunciation error to be provided. Two novel methods to model speech attributes are further proposed in this thesis, a frame-based (phoneme-alignment) method leveraging the Multi-Task Learning (MTL) criterion and training a separate model for each attribute, and an alignment-free jointly-learnt method based on the Connectionist Temporal Classification (CTC) sequence to sequence criterion. The proposed techniques have been evaluated using standard and publicly accessible adult and child speech corpora, while the MDD method has been validated using L2 speech corpora