Abstract
The presence of a corresponding talking face has been shown to significantly improve speech intelligibility in noisy conditions and for hearing impaired population. In this paper, we present a system that can generate landmark points of a talking face from an acoustic speech in real time. The system uses a long short-term memory (LSTM) network and is trained on frontal videos of 27 different speakers with automatically extracted face landmarks. After training, it can produce talking face landmarks from the acoustic speech of unseen speakers and utterances. The training phase contains three key steps. We first transform landmarks of the first video frame to pin the two eye points into two predefined locations and apply the same transformation on all of the following video frames. We then remove the identity information by transforming the landmarks into a mean face shape across the entire training dataset. Finally, we train an LSTM network that takes the first- and second-order temporal differences of the log-mel spectrogram as input to predict face landmarks in each frame. We evaluate our system using the mean-squared error (MSE) loss of landmarks of lips between predicted and ground-truth landmarks as well as their first- and second-order temporal differences. We further evaluate our system by conducting subjective tests, where the subjects try to distinguish the real and fake videos of talking face landmarks. Both tests show promising results.
Z. Duan—This work is supported by the University of Rochester Pilot Award Program in AR/VR and the National Science Foundation grant No. 1741471.
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References
Blamey, P.J., Pyman, B.C., Clark, G.M., Dowell, R.C., Gordon, M., Brown, A.M., Hollow, R.D.: Factors predicting postoperative sentence scores in postlinguistically deaf adult cochlear implant patients. Ann. Otol. Rhinol. Laryngol. 101(4), 342–348 (1992)
Brand, M.: Voice puppetry. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, pp. 21–28. ACM Press/Addison-Wesley Publishing Co. (1999)
Cassidy, S., Stenger, B., Dongen, L.V., Yanagisawa, K., Anderson, R., Wan, V., Baron-Cohen, S., Cipolla, R.: Expressive visual text-to-speech as an assistive technology for individuals with autism spectrum conditions. Comput. Vis. Image Underst. 148, 193–200 (2016)
Choi, K., Luo, Y., Hwang, J.N.: Hidden Markov model inversion for audio-to-visual conversion in an MPEG-4 facial animation system. J. VLSI Signal Process. Syst. Signal Image Video Technol. 29, 51–61 (2001)
Chung, J.S., Jamaludin, A., Zisserman, A.: You said that? (2017). arXiv preprint: arXiv:1705.02966
Cooke, M., Barker, J., Cunningham, S., Shao, X.: An audio-visual corpus for speech perception and automatic speech recognition. J. Acoust. Soc. Am. 120(5), 2421–2424 (2006)
Cosker, D., Marshall, D., Rosin, P.L., Hicks, Y.: Speech driven facial animation using a Hidden Markov coarticulation model. In: Proceedings of the 17th International Conference on Pattern Recognition (ICPR), vol. 1, pp. 128–131. IEEE (2004)
Cosker, D., Marshall, D., Rosin, P., Hicks, Y.: Video realistic talking heads using hierarchical non-linear speech-appearance models, Mirage, France, vol. 147 (2003)
Dodd, B.E., Campbell, R.E.: Hearing by Eye: The Psychology of Lip-Reading. Lawrence Erlbaum Associates, Inc., Hillsdale (1987)
Fan, B., Wang, L., Soong, F.K., Xie, L.: Photo-real talking head with deep bidirectional LSTM. In: International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4884–4888. IEEE (2015)
Garofalo, J.S., Lamel, L.F., Fisher, W.M., Fiscus, J.G., Pallett, D.S., Dahlgren, N.L.: The darpa timit acoustic-phonetic continuous speech corpus CD-ROM. Linguistic Data Consortium (1993)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Maddox, R.K., Atilgan, H., Bizley, J.K., Lee, A.K.: Auditory selective attention is enhanced by a task-irrelevant temporally coherent visual stimulus in human listeners. eLife 4 (2015)
Mallick, S.: Face morph using opencv c++/python (2016). http://www.learnopencv.com/face-morph-using-opencv-cpp-python/
Pham, H.X., Cheung, S., Pavlovic, V.: Speech-driven 3d facial animation with implicit emotional awareness: a deep learning approach. In: The 1st DALCOM Workshop, CVPR (2017)
Pham, H.X., Wang, Y., Pavlovic, V.: End-to-end learning for 3d facial animation from raw waveforms of speech (2017). arXiv preprint: arXiv:1710.00920
Richie, S., Warburton, C., Carter, M.: Audiovisual database of spoken American English. Linguistic Data Consortium (2009)
Suwajanakorn, S., Seitz, S.M., Kemelmacher-Shlizerman, I.: Synthesizing Obama: learning lip sync from audio. ACM Trans. Graph. (TOG) 36(4), 95 (2017)
Terissi, L.D., Gómez, J.C.: Audio-to-visual conversion via HMM inversion for speech-driven facial animation. In: Zaverucha, G., da Costa, A.L. (eds.) SBIA 2008. LNCS (LNAI), vol. 5249, pp. 33–42. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88190-2_9
Tillman, T.W., Carhart, R.: An expanded test for speech discrimination utilizing CNC monosyllabic words: Northwestern University auditory test no. 6. Technical report, Northwestern University Evanston Auditory Research Lab (1966)
Wan, V., Anderson, R., Blokland, A., Braunschweiler, N., Chen, L., Kolluru, B., Latorre, J., Maia, R., Stenger, B., Yanagisawa, K., et al.: Photo-realistic expressive text to talking head synthesis. In: INTERSPEECH, pp. 2667–2669 (2013)
Wang, L., Han, W., Soong, F.K., Huo, Q.: Text driven 3d photo-realistic talking head. In: Twelfth Annual Conference of the International Speech Communication Association (2011)
Xie, L., Liu, Z.Q.: A coupled HMM approach to video-realistic speech animation. Pattern Recogn. 40, 2325–2340 (2007)
Zhang, X., Wang, L., Li, G., Seide, F., Soong, F.K.: A new language independent, photo-realistic talking head driven by voice only. In: Interspeech, pp. 2743–2747 (2013)
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Eskimez, S.E., Maddox, R.K., Xu, C., Duan, Z. (2018). Generating Talking Face Landmarks from Speech. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_35
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