Automatic segmentation and labeling of speech based on Hidden Markov Models
Speech Communication
Lip-motion analysis for speech segmentation in noise
Speech Communication
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Phonetic alignment: speech synthesis-based vs. viterbi-based
Speech Communication
The use of articulator motion information in automatic speech segmentation
Speech Communication
On Using Multiple Models for Automatic Speech Segmentation
IEEE Transactions on Audio, Speech, and Language Processing
Analysis of lip geometric features for audio-visual speech recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Bimodal automatic speech segmentation using visual information together with audio data is introduced. The accuracy of automatic segmentation directly affects the quality of speech processing systems using the segmented database. The collaboration of audio and visual data results in lower average absolute boundary error between the manual segmentation and automatic segmentation results. The information from two modalities are fused at the feature level and used in a HMM based speech segmentation system. A Turkish audiovisual speech database has been prepared and used in the experiments. The average absolute boundary error decreases up to 18% by using different audiovisual feature vectors. The benefits of incorporating visual information are discussed for different phoneme boundary types. Each audiovisual feature vector results in a different performance at different types of phoneme boundaries. The average absolute boundary error decreases by approximately 25% by using audiovisual feature vectors selectively for different boundary classes. Visual data is collected using an ordinary webcam. The proposed method is very convenient to be used in practice.