But, contrary to our hypotheses, the performance in the audio-visual recognition test (i.e., the reverse direction) was similar after using both learning methods.
There was also a main effect of the direction of association [F 1,28) = 26.38 ; p<.0001; with R2 = .49]: Participants performed better in the visuo-auditory recognition test (M = 7.22 and SD = 2.18) than in the audio-visual recognition test (M = 5.47 and SD = 2.19).
Voice recognition systems have a mixed record in the noisy environment of the automobile, but I.B.M. is looking to supplement its version with audio visual speech recognition (A.V.S.R ., essentially video images linked to a computer that reads lips.
Audio-visual speech recognition which uses dynamic visual lip and audio information has been studied as a technique for robust speech recognition in noisy environments.
Audio-visual speech recognition systems significantly outperform their audio-only counterparts, especially under ideal visual and noisy audio conditions.
In this article, we study the adaptation of visual and audio-visual speech recognition systems to non-ideal visual conditions.
Previously, we have proposed audio-visual speech recognition using a visual feature extracted from an active appearance model [10, 11].
Lewis et al. shed some light on audio-visual speech recognition systems using dispersion measures as the integration weights [16].
The first category consists of naturally available modalities such as audio and video used in automatic audio-visual speech recognition (AAVSR) systems [14].
This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments.
The two soft belief functions, namely, BHT-SB and MHT-SB are applied to the speaker diarization and audio-visual speech recognition tasks, respectively.
