Detalle del congreso

Autores: Sad, Gonzalo; Terissi, Lucas Daniel; Gómez, Juan Carlos.

Resumen: In this work, new multi-classifier schemes for isolated Word speech recognition based on the combination of standard Hidden Markov Models (HMMs) and Complementary Gaussian Mixture Models (CGMMs) are proposed. Typically, in speech recognition systems, each Word or phoneme in the vocabulary is represented by a model trained with samples of each particular class. The recognition is then performed by computing which model best represents the input word/phoneme to be classified. In this paper, a novel classification strategy based on complementary class models is presented. A complementary model to a particular class j refers to a model that is trained with instances of all the considered clases, excepting the ones associated to that class j. The classification schemes proposed in this paper are evaluated over two audio-visual speech databases, considering acoustic noisy conditions. Experimental results show that improvements in the recognition rates through a wide range of signal to noise ratios (SNRs) are achieved with the proposed classification methodologies.

Tipo de reunión: Simposio.

Tipo de trabajo: Artículo Completo.

Producción: Combination of Standard and Complementary Models for Audio-Visual Speech Recognition.

Reunión científica: ASAI 2015, 16º Simposio Argentino de Inteligencia Artificial.

Lugar: Rosario.

Institución organizadora: SADIO, CIFASIS - UNR.

Publicado: Sí

Lugar publicación: Rosario

Mes de reunión: 9

Año: 2015.