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

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 classes, 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.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Speech Recognition

Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes