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Technology and Testing: Improving Educational and Psychological Measurement

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Institución detectada	Año de publicación	Navegá	Descargá	Solicitá
No requiere		Directory of Open access Books

Cobertura temática: Ciencias sociales - Psicología y ciencias cognitivas - Educación

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Technology, Mind, and Behavior

ISSNs 2689-0208 (en línea)

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Institución detectada	Período	Navegá	Descargá	Solicitá
No requiere	desde ene. 2020 / hasta may. 2024	Directory of Open Access Journals

Cobertura temática: Ingeniería y tecnología - Psicología y ciencias cognitivas

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Técnicas de aprendizaje maquinal para separación ciega de fuentes sonoras con aplicación al reconocimiento automático del habla

Autores/as: Leandro Ezequiel Di Persia ; Diego Humberto Milone ; Juan Cosseau ; Carlos Muravchik ; Juan Carlos Gómez ; Leonardo Luis Giovanini ; Masuzo Yanagida

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Institución detectada	Año de publicación	Navegá	Descargá	Solicitá
No requiere	2009	Biblioteca Virtual de la Universidad Nacional del Litoral (SNRD)

Cobertura temática: Psicología y ciencias cognitivas

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In the last decades a new problem related to machine learning and signal processing has emerged in many disciplines: the blind source separation problem. The blind source separation technique aims to segregate the sources that contribute to some variation of a physical quantity, given a set of measurements of the global variation produced by all sources at a time. One particular application of the blind source separation methods is the Automatic Speech Recognition, which can be defined as the task of determining the text that corresponds to a given spoken utterance. This kind of systems have reached a maturity point but they still suffer from a strong drawback: they cannot adequatelly manage the existence of noise or competing sources in the input. This doctoral dissertation presents several advances in the technique of audio source separation in reverberat conditions, using independent component analysis in the time-frequency domain. Three methods were developed in order to produce a better quality of separation and, at the same time, to reduce the processing times. The proposed algorithms were evaluated under realistic conditions such as different environments and different kind and power of competing sources. For this purpose we used two evaluation alternatives, objective quality measures of the resulting signal and the performance in the application of interest, that is, automatic speech recognition. The results for the different approaches show the possibility of getting through the dilemma between resulting quality and requiered processing time, converging to a very fast and high quality separation method.

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