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Viseme

About: Viseme is a research topic. Over the lifetime, 865 publications have been published within this topic receiving 17889 citations.


Papers
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Journal ArticleDOI
TL;DR: This special issue was inspired by the 2013 Workshop on Speech Production in Automatic Speech Recognition in Lyon, France, and this introduction provides an overview of the included papers in the context of the current research landscape.

4 citations

DOI
01 Jan 2010
TL;DR: This paper investigates alternative representations for visemes, produced using motion-captured data, in conjunction with a constraint-based approach for visual speech production, and shows that using Visemes which incorporate more contextual information produces better results that using static pose viseme.
Abstract: A common approach to producing visual speech is to interpolate the parameters describing a sequence of mouth shapes, known as visemes, where visemes are the visual counterpart of phonemes. A single viseme typically represents a group of phonemes that are visually similar. Often these visemes are based on the static poses used in producing a phoneme. In this paper we investigate alternative representations for visemes, produced using motion-captured data, in conjunction with a constraint-based approach for visual speech production. We show that using visemes which incorporate more contextual information produces better results that using static pose visemes.

4 citations

Book ChapterDOI
01 Jan 1996
TL;DR: A facial animation program with an open input text vocabulary for use as a training aid for speechreading by introducing appropriate motion models for those visual articulatory movements that are relevant for the process of speechreading.
Abstract: Goal of this paper is to introduce appropriate motion models for those visual articulatory movements that are relevant for the process of speechreading, and with this, design a facial animation program with an open input text vocabulary for use as a training aid for speechreading

4 citations

Proceedings Article
01 Jan 1997
TL;DR: A re-entry modeling of missing phonemes which are lost during search process using a multiple pronunciation dictionary where pronunciations are added using HMM-state confusion characteristics to improve word recognition rates.
Abstract: In our previous work, we proposed a re-entry modeling of missing phonemes which are lost during search process. In the re-entry modeling, the recognition results are postprocessed and originally recognized phoneme sequences are converted to new phoneme sequences using HMM-state confusion characteristics spanning several phonemes. We con rmed that HMM-state confusions are e ective for the re-entry modeling. In this paper, we propose a re-entry modeling during recognition using a multiple pronunciation dictionary where pronunciations are added using HMM-state confusion characteristics. The pronunciations are added considering partof-speech (POS) dependency of confusion characteristics. As a result of continuous recognition experiments, we con rmed that the following two points are e ective to improve word recognition rates: (1) confusions are expressed by HMM-state sequences, (2) pronunciations are added considering part-of-speech dependency of confusion characteristics.

3 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20237
202212
202113
202039
201919
201822