Xing Fan, Carlos Busso and John H.L. Hansen
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1 Xing Fan, Carlos Busso and John H.L. Hansen Center for Robust Speech Systems (CRSS) Erik Jonsson School of Engineering & Computer Science Department of Electrical Engineering University of Texas at Dallas Richardson, Texas , U.S.A. EUSIPCO 2011 August 29 - September Barcelona, Spain {xxf064000, busso, John.Hansen}@utdallas.edu Slide 1 EUSIPCO 2011, Barcelona Spain, August 29-September
2 Absence of periodic excitation Acoustic Formant shifting in F1, F2 Domain Low volume and longer duration Significant degradation in performance for ASR system trained with neutral speech {xxf064000, busso, Slide 2 EUSIPCO 2011, Barcelona Spain, August 29-September
3 WHISPER-NEUTRAL Audio-Lip DATA 1 male native American English speaking subject 300 digits numbers from 0-9 are randomly ordered and read by the subject in both whispered and neutral mode. Both audio and visual information are recorded by a camera. The video is of the size 720*576, captures in color at a rate of 25 frames/sec The audio is collected with the video at a sample rate of 44.1 khz with microphone in the camera ~70cm from the subject, and down-sampled to 16 KHz {xxf064000, busso, John.Hansen}@utdallas.edu Slide 3 EUSIPCO 2011, Barcelona Spain, August 29-September
4 Pre-processing (extraction of ROI) Step1 : Using gravity center detection to find the center of the lips Step2 : Using RGB color vector to detect the lip boundary Use developing lip sample to train a Lip color GMM For each input image, each pixel will test the GMM, the boundary will be determined by the output score matrix {xxf064000, busso, John.Hansen}@utdallas.edu Slide 4 EUSIPCO 2011, Barcelona Spain, August 29-September
5 Eigenlips: A total number of 400 lip samples are employed for calculating the eigenlips {xxf064000, busso, John.Hansen}@utdallas.edu Slide 5 EUSIPCO 2011, Barcelona Spain, August 29-September
6 Steps for calculating the eigenlips Calculate the mean lip image structure Normalizing of each image Calculate the covariance matrix C Obtain the eigenvectors and eigenvalues of C Choose and sort the eigenvalues and find the corresponding eigenvectors Eigenvectors with top 15 largest eigenvalues are chosen as eigenlips {xxf064000, busso, John.Hansen}@utdallas.edu Slide 6 EUSIPCO 2011, Barcelona Spain, August 29-September
7 Mean Lip Original Lips Lip Reconstruction with Eigenlips Projection on to the eigenspace {xxf064000, busso, Slide 7 EUSIPCO 2011, Barcelona Spain, August 29-September
8 HMM Structure used in this study MFCCs EIGENLIPS {xxf064000, busso, Slide 8 EUSIPCO 2011, Barcelona Spain, August 29-September
9 Audio and Video HMMs are trained independent in this study. Audio: Training feature: MFCC_0_D_A (39d) State num: 16 states (2 non-emitting state) Mixture number: 1 Video: Training feature: eigenlips_d (30d) State num: 7 states (2 non-emitting state) Mixture number: 1 {xxf064000, busso, John.Hansen}@utdallas.edu Slide 9 EUSIPCO 2011, Barcelona Spain, August 29-September
10 Decoding: Synchrony is constrained on the boundary of each word Scores are log-linearly combined {xxf064000, busso, Slide 10 EUSIPCO 2011, Barcelona Spain, August 29-September
11 Adjusting the value of the weight of the audio score versus lip score, we have word accuracy: (training data only contains neutral speech) {xxf064000, busso, Slide 11 EUSIPCO 2011, Barcelona Spain, August 29-September
12 Overall Word accuracy: Stream training test Word Accuracy(%) Audio data neutral neutral 98.7 Audio data whisper whisper 83.3 Audio data neutral whisper 42.7 Video data neutral neutral % Video data whisper whisper 68.0 Video data neutral whisper 54.7 combined neutral whisper % Audio based system achieves good baseline 98.7% Testing with whisper audio significant ASR performance loss 56% Combine Audio-Visual improved performance to 79.7% {xxf064000, busso, Slide 12 EUSIPCO 2011, Barcelona Spain, August 29-September
13 A small digit corpus is developed for an exploratory study of audio-visual speech recognition for whispered speech. An eigenlip based feature extraction method is applied for visual data Multistream framework is built using audio and video stream HMMs Significant improvement in word accuracy is presented using this multi-stream model system {xxf064000, busso, Slide 13 EUSIPCO 2011, Barcelona Spain, August 29-September
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