Identification of phonological processes in preschool children's single-word productions

Speech and language therapists (SLTs) often refer to phonological data norms as part of their assessment protocols in evaluating the communication skills of the pre-school child. There is a variety of norms available and although broadly similar, differences are embedded within their definitions of mastery of the adult target system. Presence of velar fronting, stopping of affricates and [s] reduction in the dataset was found to mirror previous research. However, there was a lower than expected incidence by age groups of palato-alveolar fronting, stopping of fricatives and obstruent cluster reduction

From Holistic to Discrete Speech Sounds: The Blind Snow-Flake Maker Hypothesis

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents

The Self-Organization of Speech Sounds

The speech code is a vehicle of language: it defines a set of forms used by a community to carry information. Such a code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is discrete and compositional, shared by all the individuals of a community but different across communities, and phoneme inventories are characterized by statistical regularities. How can a speech code with these properties form? We try to approach these questions in the paper, using the ``methodology of the artificial''. We build a society of artificial agents, and detail a mechanism that shows the formation of a discrete speech code without pre-supposing the existence of linguistic capacities or of coordinated interactions. The mechanism is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices leads to the formation of a speech code that has properties similar to the human speech code. This result relies on the self-organizing properties of a generic coupling between perception and production within agents, and on the interactions between agents. The artificial system helps us to develop better intuitions on how speech might have appeared, by showing how self-organization might have helped natural selection to find speech

vocal signal analysis in patients affected by multiple sclerosis

Abstract Multiple Sclerosis (MS) is one of the most common neurodegenerative disorder that presents specific manifestations among which the impaired speech (known also as dysarthria). The evaluation of the speech plays a crucial role in the diagnosis and follow-up since the identification of anomalous patterns in vocal signal may represent a valid support to physician in diagnosis and monitoring of these neurological diseases. In this contribution, we present a method to perform voice analysis of neurologically impaired patients affected by MS aiming to early detection, differential diagnosis, and monitoring of disease progression. This method integrates two well-known methodologies to support the health structure in MS diagnosis in clinical practice. Acoustic analysis and vowel metric methodologies have been considered to implement this procedure to better define the pathological voices compared to healthy voices. Specifically, the method acquires and analyzes vocal signals performing features extraction and identifying possible important patterns useful to associate impaired speech with this neurological disease. The contribution consists in furnishing to physician a guide method to support MS trend. As result, this method furnishes patterns that could be valid indicators for physician in monitoring of patients affected by MS. Moreover, the procedure is appropriate to be used in early diagnosis that is critical in order to improve the patient's quality of life

From Analogue to Digital Vocalizations

Sound is a medium used by humans to carry information. The existence of this kind of medium is a pre-requisite for language. It is organized into a code, called speech, which provides a repertoire of forms that is shared in each language community. This code is necessary to support the linguistic interactions that allow humans to communicate. How then may a speech code be formed prior to the existence of linguistic interactions? Moreover, the human speech code is characterized by several properties: speech is digital and compositional (vocalizations are made of units re-used systematically in other syllables); phoneme inventories have precise regularities as well as great diversity in human languages; all the speakers of a language community categorize sounds in the same manner, but each language has its own system of categorization, possibly very different from every other. How can a speech code with these properties form? These are the questions we will approach in the paper. We will study them using the method of the artificial. We will build a society of artificial agents, and study what mechanisms may provide answers. This will not prove directly what mechanisms were used for humans, but rather give ideas about what kind of mechanism may have been used. This allows us to shape the search space of possible answers, in particular by showing what is sufficient and what is not necessary. The mechanism we present is based on a low-level model of sensory-motor interactions. We show that the integration of certain very simple and non language-specific neural devices allows a population of agents to build a speech code that has the properties mentioned above. The originality is that it pre-supposes neither a functional pressure for communication, nor the ability to have coordinated social interactions (they do not play language or imitation games). It relies on the self-organizing properties of a generic coupling between perception and production both within agents, and on the interactions between agents

Análisis cepstral y la transformada de Hilbert-Huang para la detección automática de la enfermedad de Parkinson

Most patients with Parkinson’s Disease (PD) develop speech deficits, including reduced sonority, altered articulation, and abnormal prosody. This article presents a methodology to automatically classify patients with PD and Healthy Control (HC) subjects. In this study, the Hilbert-Huang Transform (HHT) and Mel-Frequency Cepstral Coefficients (MFCCs) were considered to model modulated phonations (changing the tone from low to high and vice versa) of the vowels /a/, /i/, and /u/. The HHT was used to extract the first two formants from audio signals with the aim of modeling the stability of the tongue while the speakers were producing modulated vowels. Kruskal-Wallis statistical tests were used to eliminate redundant and non-relevant features in order to improve classification accuracy. PD patients and HC subjects were automatically classified using a Radial Basis Support Vector Machine (RBF-SVM). The results show that the proposed approach allows an automatic discrimination between PD and HC subjects with accuracies of up to 75 % for women and 73 % for men.La mayoría de las personas con la enfermedad de Parkinson (EP) desarrollan varios déficits del habla, incluyendo sonoridad reducida, alteración de la articulación y prosodia anormal. Este artículo presenta una metodología que permite la clasificación automática de pacientes con EP y sujetos de control sanos (CS). Se considera que la transformada de Hilbert-Huang (THH) y los Coeficientes Cepstrales en las frecuencias de Mel modelan las fonaciones moduladas (cambiando el tono de bajo a alto y de alto a bajo) de las vocales /a/, /i/, y /u/. La THH se utiliza para extraer los dos primeros formantes de las señales de audio, con el objetivo de modelar la estabilidad de la lengua mientras los hablantes producen vocales moduladas. Pruebas estadísticas de Kruskal-Wallis se utilizan para eliminar características redundantes y no relevantes, con el fin de mejorar la precisión de la clasificación. La clasificación automática de sujetos con EP vs. CS se realiza mediante una máquina de soporte vectorial de base radial. De acuerdo con los resultados, el enfoque propuesto permite la discriminación automática de sujetos con EP vs. CS con precisiones de hasta el 75 % para los hombres y 73 % para las mujeres

Acoustic Changes during Passage Reading in Speakers with Parkinson\u27s Disease

Purpose: The purpose of this study was to evaluate speech changes in Parkinson’s disease (PD) while reading a passage, using both local (i.e., segment level) and global (i.e., utterance level) acoustic measures. Methods: 20 speakers participated in the study (10 PD, 10 neurologically healthy controls). The speakers were asked to read The Caterpillar passage in a conversational mode. A total of five acoustic measures were included (local: vowel duration, Euclidean distance between corner vowels and schwa; global: articulation rate, F0/intensity range). These acoustic measures were compared between two sentences located in the two positions within the paragraph, initial and final. Results: The findings indicated (1) overall speech differences between the two groups such as increased vowel duration and reduced vowel contrast and (2) speech differences between the beginning and end of the passage such as increased articulation rate toward the end. In addition, the results revealed that unlike control speakers, speakers with PD did not show a greater F0 and intensity range in the end compared to the beginning of the passage, which points a limited capability of prosody modulations in PD and its apparent pattern toward the end of passage reading. Discussion: Findings of this study support the notion that within- or across-task acoustic variation should be considered in speech sampling in clinical practice and research

A limited speech recognition system 2 Final report

Limited speech recognition system for computer voice lin