Publication

Class-specific classifiers in audio-visual speech recognition

Jean-Philippe Thiran, Virginia Estellers Casas
2010
Conference Papers

Abstract

Class-specific classifiers for audio, visual and audio-visual speech recognition systems are developed and compared with traditional classifiers. We use state- of-the-art feature extraction methods and show the benefits of a class-specific classifier on each modality in speech recognition experiments.

Official source

https://infoscience.epfl.ch/entities/publication/8f0f3985-4693-4785-9507-bef61f1a0441

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Jean-Philippe Thiran, Virginia Estellers Casas
2010
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/8f0f3985-4693-4785-9507-bef61f1a0441

About this result

Related concepts (8)

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Multimodal interaction

Multimodal interaction provides the user with multiple modes of interacting with a system. A multimodal interface provides several distinct tools for input and output of data. Multimodal human-computer interaction refers to the "interaction with the virtual and physical environment through natural modes of communication", This implies that multimodal interaction enables a more free and natural communication, interfacing users with automated systems in both input and output.

Learning classifier system

Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic algorithm) with a learning component (performing either supervised learning, reinforcement learning, or unsupervised learning). Learning classifier systems seek to identify a set of context-dependent rules that collectively store and apply knowledge in a piecewise manner in order to make predictions (e.g. behavior modeling, classification, data mining, regression, function approximation, or game strategy).

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