Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Decoding visemes: improving machine lipreading   Machine lipreading (MLR) is speech recognition from visual cues and a niche
research problem in speech processing & computer vision. Current challenges
fall into two groups: the content of the video, such as rate of speech or; the
parameters of the video recording e.g, video resolution. We show that HD video
is not needed to successfully lipread with a computer. The term "viseme" is
used in machine lipreading to represent a visual cue or gesture which
corresponds to a subgroup of phonemes where the phonemes are visually
indistinguishable. A phoneme is the smallest sound one can utter, because there
are more phonemes per viseme, maps between units show a many-to-one
relationship. Many maps have been presented, we compare these and our results
show Lee's is best. We propose a new method of speaker-dependent
phoneme-to-viseme maps and compare these to Lee's. Our results show the
sensitivity of phoneme clustering and we use our new knowledge to augment a
conventional MLR system. It has been observed in MLR, that classifiers need
training on test subjects to achieve accuracy. Thus machine lipreading is
highly speaker-dependent. Conversely speaker independence is robust
classification of non-training speakers. We investigate the dependence of
phoneme-to-viseme maps between speakers and show there is not a high
variability of visemes, but there is high variability in trajectory between
visemes of individual speakers with the same ground truth. This implies a
dependency upon the number of visemes within each set for each individual. We
show that prior phoneme-to-viseme maps rarely have enough visemes and the
optimal size, which varies by speaker, ranges from 11-35. Finally we decode
from visemes back to phonemes and into words. Our novel approach uses the
optimum range visemes within hierarchical training of phoneme classifiers and
demonstrates a significant increase in classification accuracy.

15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: chao-dyn
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: q-alg