99,315 research outputs found
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
Combining Multiple Views for Visual Speech Recognition
Visual speech recognition is a challenging research problem with a particular
practical application of aiding audio speech recognition in noisy scenarios.
Multiple camera setups can be beneficial for the visual speech recognition
systems in terms of improved performance and robustness. In this paper, we
explore this aspect and provide a comprehensive study on combining multiple
views for visual speech recognition. The thorough analysis covers fusion of all
possible view angle combinations both at feature level and decision level. The
employed visual speech recognition system in this study extracts features
through a PCA-based convolutional neural network, followed by an LSTM network.
Finally, these features are processed in a tandem system, being fed into a
GMM-HMM scheme. The decision fusion acts after this point by combining the
Viterbi path log-likelihoods. The results show that the complementary
information contained in recordings from different view angles improves the
results significantly. For example, the sentence correctness on the test set is
increased from 76% for the highest performing single view () to up to
83% when combining this view with the frontal and view angles
- …