Publication detail

Multimodal Phoneme Recognition of Meeting Data

MOTLÍČEK, P., ČERNOCKÝ, J.

Original Title

Multimodal Phoneme Recognition of Meeting Data

Type

journal article - other

Language

English

Original Abstract

This paper describes experiments in automatic recognition of context-independent phoneme strings from meeting data using audio-visual features. Visual features are known to improve accuracy and noise robustness of automatic speech recognizers. However, many problems appear when not "visually clean'' data is provided, such as data without limited variation in the speaker's frontal pose, lighting conditions, background, etc. The goal of this work was to test whether visual information can be helpful for recognition of phonemes using neural nets. While the audio part is fixed and uses standard Mel filter-bank energies, different features describing the video were tested: average brightness, DCT coefficients extracted from region-of-interest (ROI), optical flow analysis and lip-position features. The recognition was evaluated on a sub-set of IDIAP meeting room data. We have seen small improvement when compared to purely audio-recognition, but further work needs to be done especially concerning the determination of reliability of video features.

Keywords

speech processing, audio-video processing, feature extraction, pattern recognition

Authors

MOTLÍČEK, P., ČERNOCKÝ, J.

RIV year

2004

Released

8. 9. 2004

ISBN

0302-9743

Periodical

Lecture Notes in Computer Science

Year of study

2004

Number

3206

State

Federal Republic of Germany

Pages from

379

Pages to

384

Pages count

6

URL

http://www.springerlink.com/index/U0DJ8GHXE220LX81

BibTex

@article{BUT45741,
  author="Petr {Motlíček} and Jan {Černocký}",
  title="Multimodal Phoneme Recognition of Meeting Data",
  journal="Lecture Notes in Computer Science",
  year="2004",
  volume="2004",
  number="3206",
  pages="6",
  issn="0302-9743",
  url="http://www.springerlink.com/index/U0DJ8GHXE220LX81"
}