Viseme

About: Viseme is a research topic. Over the lifetime, 865 publications have been published within this topic receiving 17889 citations.

End-to-end speech recognition

Abstract: Embodiments of end-to-end deep learning systems and methods are disclosed to recognize speech of vastly different languages, such as English or Mandarin Chinese. In embodiments, the entire pipelines of hand-engineered components are replaced with neural networks, and the end-to-end learning allows handling a diverse variety of speech including noisy environments, accents, and different languages. Using a trained embodiment and an embodiment of a batch dispatch technique with GPUs in a data center, an end-to-end deep learning system can be inexpensively deployed in an online setting, delivering low latency when serving users at scale.

Real-time synthesis of Chinese visual speech and facial expressions using MPEG-4 FAP features in a three-dimensional avatar.

Abstract: This paper describes our initial work in developing a real-time audio-visual Chinese speech synthesizer with a 3D expressive avatar. The avatar model is parameterized according to the MPEG-4 facial animation standard [1]. This standard offers a compact set of facial animation parameters (FAPs) and feature points (FPs) to enable realization of 20 Chinese visemes and 7 facial expressions (i.e. 27 target facial configurations). The Xface [2] open source toolkit enables us to define the influence zone for each FP and the deformation function that relates them. Hence we can easily animate a large number of coordinates in the 3D model by specifying values for a small set of FAPs and their FPs. FAP values for 27 target facial configurations were estimated from available corpora. We extended the dominance blending approach to effect animations for coarticulated visemes superposed with expression changes. We selected six sentiment-carrying text messages and synthesized expressive visual speech (for all expressions, in randomized order) with neutral audio speech. A perceptual experiment involving 11 subjects shows that they can identify the facial expression that matches the text message’s sentiment 85% of the time.

Multi-view Automatic Lip-Reading Using Neural Network

Abstract: It is well known that automatic lip-reading (ALR), also known as visual speech recognition (VSR), enhances the performance of speech recognition in a noisy environment and also has applications itself. However, ALR is a challenging task due to various lip shapes and ambiguity of visemes (the basic unit of visual speech information). In this paper, we tackle ALR as a classification task using end-to-end neural network based on convolutional neural network and long short-term memory architecture. We conduct single, cross, and multi-view experiments in speaker independent setting with various network configuration to integrate the multi-view data. We achieve 77.9%, 83.8%, and 78.6% classification accuracies in average on single, cross, and multi-view respectively. This result is better than the best score (76%) of preliminary single-view results given by ACCV 2016 workshop on multi-view lip-reading/audio-visual challenges. It also shows that additional view information helps to improve the performance of ALR with neural network architecture.