Viseme

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

Cross-stream observation dependencies for multi-stream speech recognition.

Abstract: This paper extends prior work in multi-stream modeling by introducing cross-stream observation dependencies and a new discriminative criterion for selecting such dependencies. Experimental results combining short-term PLP features with longterm TRAP features show gains associated with a multi-stream model with partial state asynchrony over a baseline HMM. Frame-based analyses show significant discriminant information in the added cross-stream dependencies, but so far there are only small gains in recognition accuracy.

Alternative Visual Units for an Optimized Phoneme-Based Lipreading System

Abstract: Lipreading is understanding speech from observed lip movements. An observed series of lip motions is an ordered sequence of visual lip gestures. These gestures are commonly known, but as yet are not formally defined, as `visemes'. In this article, we describe a structured approach which allows us to create speaker-dependent visemes with a fixed number of visemes within each set. We create sets of visemes for sizes two to 45. Each set of visemes is based upon clustering phonemes, thus each set has a unique phoneme-to-viseme mapping. We first present an experiment using these maps and the Resource Management Audio-Visual (RMAV) dataset which shows the effect of changing the viseme map size in speaker-dependent machine lipreading and demonstrate that word recognition with phoneme classifiers is possible. Furthermore, we show that there are intermediate units between visemes and phonemes which are better still. Second, we present a novel two-pass training scheme for phoneme classifiers. This approach uses our new intermediary visual units from our first experiment in the first pass as classifiers; before using the phoneme-to-viseme maps, we retrain these into phoneme classifiers. This method significantly improves on previous lipreading results with RMAV speakers.

A mouth full of words: Visually consistent acoustic redubbing

Abstract: This paper introduces a method for automatic redubbing of video that exploits the many-to-many mapping of phoneme sequences to lip movements modelled as dynamic visemes [1]. For a given utterance, the corresponding dynamic viseme sequence is sampled to construct a graph of possible phoneme sequences that synchronize with the video. When composed with a pronunciation dictionary and language model, this produces a vast number of word sequences that are in sync with the original video, literally putting plausible words into the mouth of the speaker. We demonstrate that traditional, many-to-one, static visemes lack flexibility for this application as they produce significantly fewer word sequences. This work explores the natural ambiguity in visual speech and offers insight for automatic speech recognition and the importance of language modeling.

A constraint-based approach to visual speech for a Mexican-Spanish talking head

Abstract: A common approach to produce visual speech is to interpolate the parameters describing a sequence of mouth shapes, known as visemes, where a viseme corresponds to a phoneme in an utterance. The interpolation process must consider the issue of context-dependent shape, or coarticulation, in order to produce realistic-looking speech. We describe an approach to such pose-based interpolation that deals with coarticulation using a constraint-based technique. This is demonstrated using a Mexican-Spanish talking head, which can vary its speed of talking and produce coarticulation effects.