10,555 research outputs found
Visual Speech Recognition using Active Shape Models and Hidden Markov Models
This paper describes a novel approach for visual speech recognition. The shape of the mouth is modelled by an Active Shape Model which is derived from the statistics of a training set and used to locate, track and parameterise the speaker�s lip movements. The extracted parameters representing the lip shape are modelled as continuous probability distributions and their temporal dependencies are modelled by Hidden Markov Models. We present recognition tests performed on a database of a broad variety of speakers and illumination conditions. The system achieved an accuracy of 85.42 % for a speaker independent recognition task of the first four digits using lip shape information only
Audio-visual speech recognition with background music using single-channel source separation
In this paper, we consider audio-visual speech recognition with background music. The proposed algorithm is an integration of audio-visual speech recognition and single channel source separation (SCSS). We apply the proposed algorithm to recognize spoken speech that is mixed with music signals. First, the SCSS algorithm based on nonnegative matrix factorization (NMF) and spectral masks is used to separate the audio speech signal from the background music in magnitude spectral domain. After speech audio is separated from music, regular audio-visual speech recognition (AVSR) is employed using multi-stream hidden
Markov models. Employing two approaches together, we try to improve recognition accuracy by both processing the audio signal with SCSS and supporting the recognition task with visual information. Experimental results show that combining audio-visual speech recognition with source separation gives remarkable improvements in the accuracy of the speech recognition system
Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers
We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality
Capture, Learning, and Synthesis of 3D Speaking Styles
Audio-driven 3D facial animation has been widely explored, but achieving
realistic, human-like performance is still unsolved. This is due to the lack of
available 3D datasets, models, and standard evaluation metrics. To address
this, we introduce a unique 4D face dataset with about 29 minutes of 4D scans
captured at 60 fps and synchronized audio from 12 speakers. We then train a
neural network on our dataset that factors identity from facial motion. The
learned model, VOCA (Voice Operated Character Animation) takes any speech
signal as input - even speech in languages other than English - and
realistically animates a wide range of adult faces. Conditioning on subject
labels during training allows the model to learn a variety of realistic
speaking styles. VOCA also provides animator controls to alter speaking style,
identity-dependent facial shape, and pose (i.e. head, jaw, and eyeball
rotations) during animation. To our knowledge, VOCA is the only realistic 3D
facial animation model that is readily applicable to unseen subjects without
retargeting. This makes VOCA suitable for tasks like in-game video, virtual
reality avatars, or any scenario in which the speaker, speech, or language is
not known in advance. We make the dataset and model available for research
purposes at http://voca.is.tue.mpg.de.Comment: To appear in CVPR 201
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