Continuous Speech Recognition for Clinicians

Continuous speech recognition for clinicians.

The current generation of continuous speech recognition systems claims to offer high accuracy (greater than 95 percent) speech recognition at natural speech rates (150 words per minute) on low-cost (under $2000) platforms. This paper presents a state-of-the-technology summary, along with insights the authors have gained through testing one such product extensively and other products superficial...

Improved Bayesian Training for Context-Dependent Modeling in Continuous Persian Speech Recognition

Context-dependent modeling is a widely used technique for better phone modeling in continuous speech recognition. While different types of context-dependent models have been used, triphones have been known as the most effective ones. In this paper, a Maximum a Posteriori (MAP) estimation approach has been used to estimate the parameters of the untied triphone model set used in data-driven clust...

Continuous Audio-visual Speech Recognition Continuous Audio-visual Speech Recognition

We address the problem of robust lip tracking, visual speech feature extraction, and sensor integration for audiovisual speech recognition applications. An appearance based model of the articulators, which represents linguistically important features, is learned from example images and is used to locate, track, and recover visual speech information. We tackle the problem of joint temporal model...

Robust Continuous Speech Recognition

The pnrnary objective of this basic research program is to develop robust methods and models for speaker-independent acoustic recognition of spontaneously-produced, :ontinuous speech. The work has focussed on developing accurate and detailed models of phonemes and their coarticulation for the purpose of large-vocabulary continuous speech recognition. Important goals of this work are to achieve ...

Continuous Speech Recognition