We use HMMs (hidden Markov models) to convert audio signals to a sequence of visemes. In this paper, we compare two approaches in using HMMs. In the first approach, an HMM is trained for each viseme, and the audio signals are directly recognized as a sequence of visemes.
Aug 21, 2002
We use HMMs (hidden Markov models) to convert audio signals to a sequence of visemes. In this paper, we compare two approaches in using HMMs. In the first ...
process for using hidden Markov models for audio-to-visual conversion will be outlined below. The audio-visual parameter is o = [aT w]*. Training a Train an ...
Two approaches in using HMMs (hidden Markov models) to convert audio signals to a sequence of visemes are compared and it is found that the error rates can ...
In the phonetic approach to audio-to-visual speech conversion, the transformation is done via the intermediate step of phoneme recognition. The phonemes, in ...
Audio-to-Visual Conversion Via HMM Inversion for Speech-Driven Facial ...
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In this paper, the inversion of a joint Audio-Visual Hidden Markov Model is proposed to estimate the visual information from speech data in a speech driven ...
In this paper, we address audio-to-visual conversion problems by introducing a novel Hidden Markov Model Inversion (HMMI) method. In training audio-visual HMMs, ...
The hidden Markov model inversion (HMMI) technique introduced for robust speech recognition is extended in this paper into the audio-visual feature space. Based ...
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We propose a method to exploit audio-visual cues to enable speech separation under non-stationary noise and with a single microphone. We revise and extend HMM- ...
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We propose a method to exploit audio-visual cues to enable speech separation under non-stationary noise and with a single microphone. We revise and extend HMM- ...