Predicción de la enfermedad de Parkinson utilizando redes neuronales convolucionales

La enfermedad de Parkinson (EP) es un desorden neurodegenerativo del sistema nervioso, de causa desconocida y curso crónico, progresivo e irreversible. En la actualidad se asume que los cambios patofisiológicos que permiten apreciar los síntomas de la enfermedad, no son visibles hasta al menos cuatro años luego de su inicio. Por esta causa, se buscan métodos alternativos que permitan detectar la enfermedad en forma temprana. Dado que las deficiencias del habla es uno de los síntomas de la enfermedad, esto puede dar origen a un biomarcador para el diagnóstico temprano y el monitoreo de la enfermedad. Este trabajo propone un estudio a partir del aprendizaje profundo de los espectrogramas obtenidos de señales de voz grabadas con celulares. Como objetivo se plantea realizar aportes al diagnóstico de EP, contribuyendo asimismo al conocimiento de las características de la voz afectadas por la enfermedad. Para tal fin se creará una base de datos de espectrogramas de los segmentos de audio que mejor permitan caracterizar la voz de los EP. Se desarrollarán modelos de redes neuronales convolucionales con distintas arquitecturas para distinguir los EP de los pacientes sanos, utilizando la validación adecuada para las características de dichos datos.Base de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Análisis de la capacidad articulatoria en la voz de pacientes con la enfermedad de parkinson

En este trabajo se desarrolló una metodología que permite detectar problemas en la voz de pacientes con la enfermedad del Parkinson. Fueron evaluados 100 individuos, 50 afectados con la enfermedad de Parkinson y 50 hablantes sanos. Para esto se consideraron grabaciones de voz con tareas diseñadas para analizar la transición de un sonido a otro (coarticulación). La idea central fue extraer diferentes características acústicas que entregaran información acerca de la dificultad de los pacientes para articular diferentes sonidos, las cuales se basaron principalmente en 17 energías extraídas de las bandas de Bark, los coeficientes cepstrales de Mel y los momentos espectrales. Se propusieron diferentes ejercicios de clasificación basados en métodos de aprendizaje de máquina, principalmente en máquinas de soporte vectorial, K - vecinos más cercanos y redes neuronales, buscando encontrar la mejor tasa de acierto para la diferenciación entre hablantes sanos y pacientes. Se logró una tasa de acierto de hasta un 76,00% con AuC de 0,81, en la diferenciación automática, para el análisis de segmentos VOT. Palabras claves: Enfermedad de Parkinson, Bandas de Bark, Coeficientes Cepstrales de Mel, Momentos espectrales, Voz en el tiempo, Máquinas de soporte vectorial, K-vecinos cercanos, Redes neuronales

VOCAL BIOMARKERS OF CLINICAL DEPRESSION: WORKING TOWARDS AN INTEGRATED MODEL OF DEPRESSION AND SPEECH

Speech output has long been considered a sensitive marker of a person’s mental state. It has been previously examined as a possible biomarker for diagnosis and treatment response for certain mental health conditions, including clinical depression. To date, it has been difficult to draw robust conclusions from past results due to diversity in samples, speech material, investigated parameters, and analytical methods. Within this exploratory study of speech in clinically depressed individuals, articulatory and phonatory behaviours are examined in relation to psychomotor symptom profiles and overall symptom severity. A systematic review provided context from the existing body of knowledge on the effects of depression on speech, and provided context for experimental setup within this body of work. Examinations of vowel space, monophthong, and diphthong productions as well as a multivariate acoustic analysis of other speech parameters (e.g., F0 range, perturbation measures, composite measures, etc.) are undertaken with the goal of creating a working model of the effects of depression on speech. Initial results demonstrate that overall vowel space area was not different between depressed and healthy speakers, but on closer inspection, this was due to more specific deficits seen in depressed patients along the first formant (F1) axis. Speakers with depression were more likely to produce centralised vowels along F1, as compared to F2—and this was more pronounced for low-front vowels, which are more complex given the degree of tongue-jaw coupling required for production. This pattern was seen in both monophthong and diphthong productions. Other articulatory and phonatory measures were inspected in a factor analysis as well, suggesting additional vocal biomarkers for consideration in diagnosis and treatment assessment of depression—including aperiodicity measures (e.g., higher shimmer and jitter), changes in spectral slope and tilt, and additive noise measures such as increased harmonics-to-noise ratio. Intonation was also affected by diagnostic status, but only for specific speech tasks. These results suggest that laryngeal and articulatory control is reduced by depression. Findings support the clinical utility of combining Ellgring and Scherer’s (1996) psychomotor retardation and social-emotional hypotheses to explain the effects of depression on speech, which suggest observed changes are due to a combination of cognitive, psycho-physiological and motoric mechanisms. Ultimately, depressive speech is able to be modelled along a continuum of hypo- to hyper-speech, where depressed individuals are able to assess communicative situations, assess speech requirements, and then engage in the minimum amount of motoric output necessary to convey their message. As speakers fluctuate with depressive symptoms throughout the course of their disorder, they move along the hypo-hyper-speech continuum and their speech is impacted accordingly. Recommendations for future clinical investigations of the effects of depression on speech are also presented, including suggestions for recording and reporting standards. Results contribute towards cross-disciplinary research into speech analysis between the fields of psychiatry, computer science, and speech science

Towards a clinical assessment of acquired speech dyspraxia.

No standardised assessment exists for the recognition and quantification of acquired speech dyspraxia (also called apraxia of speech, AS). This thesis aims to work towards development of such an assessment based on perceptual features. Review of previous features claimed to characterise AS and differentiate it from other acquired pronunciation problems (dysarthrias; phonemic paraphasia - PP) has proved negative. Reasons for this have been explored. A reconceptualisation of AS is attempted based on physical studies of AS, PP and the dysarthrias; their position and relationship within coalitional models of speech production; by comparison with normal action control and other dyspraxias. Contrary to the view of many it is concluded that AS and PP are dyspraxias (albeit different types). However, due to the interactive nature of speech-language production and behaviour of the vocal tract as a functional whole AS is unlikely to be distinguishable in an absolute fashion based on single speech characteristics. Rather it is predicted that pronunciation disordered groups will differ relatively on total error profiles and susceptibility to associated effects (variability; propositionality; struggle; length-complexity; latency-utterance times). Using a prototype battery and refined error transcription and analysis procedures a series of studies test predictions on three groups: spastic dysarthrics (n = 6) AS and PP without (n = 12) and with (n = 12) dysphasia. The main conclusions do not support the error profile hypotheses in any straightforward manner. Length-complexity effects and latency-utterance times fail to consistently separate groups. Variability, propositionality and struggle proved the most reliable indicators. Error profiles remain the closest indicators of speakers' intelligibility and therapeutic goals. The thesis argues for a single case approach to differential diagnosis and alternative statistical analyses to capture individual and group differences. Suggestions for changes to the prototype clinical battery and data management to effect optimal speaker differentiation conclude the work