Hidden Markov Model Based Visemes Recognition, Part I: AdaBoost Approach

Hidden Markov Model Based Visemes Recognition, Part I: AdaBoost Approach

Say Wei Foo (Nanyang Technological University, Singapore) and Liang Donga (National University of Singapore, Singapore)
Copyright: © 2009 |Pages: 30
DOI: 10.4018/978-1-60566-186-5.ch011
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Visual speech recognition is able to supplement the information of speech sound to improve the accuracy of speech recognition. A viseme, which describes the facial and oral movements that occur alongside the voicing of a particular phoneme, is a supposed basic unit of speech in the visual domain. As in phonemes, there are variations for the same viseme expressed by different persons or even by the same person. A classifier must be robust to this kind of variation. In this chapter, the author’s describe the Adaptively Boosted (AdaBoost) Hidden Markov Model (HMM) technique (Foo, 2004; Foo, 2003; Dong, 2002). By applying the AdaBoost technique to HMM modeling, a multi-HMM classifier that improves the robustness of HMM is obtained. The method is applied to identify context-independent and contextdependent visual speech units. Experimental results indicate that higher recognition accuracy can be attained using the AdaBoost HMM than that using conventional HMM.
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Brief Review of Research in Lip-Reading

The technique of retrieving speech content from visual clues such as the movement of the lips, tongue and teeth is commonly known as automatic lip-reading.

It has long been observed that the presence of visual cues such as the movement of lips, facial muscles, teeth and tongue may enhance human speech perception (Sumby, 1954). It has also been shown (Petajan, 1984; Morishima, 2002; Adjoudani, 1996; Silsbee, 1996; Tomlinson, 1996; Chen, 1998; Finn, 1988) that the performance of a purely acoustic based speech recognition system will improve with additional input from the visual speech elements, especially when the speech sound is swarmed by environmental noise. Visual speech processing can also be applied to areas such as speaker verification, multimedia telephony for the hearing impaired, cartoon animation and video games.

In 1984, Petajan developed probably the first visual speech processor. In this system, the distance of geometric measures among different mouth shapes was computed for identifying the visual representations of word productions. In 1993, Goldschen extended Petajan’s design by using Hidden Markov Model as the visual classifier. Subsequent researches on implementing visual speech processing/visual-audio integration include Neural Network (Yuhas, 1989), time-delayed Neural Network (TDNN) (Stork, 1992; Bregler, 1995), fuzzy logics (Silsbee, 1996) and Boltzmann zippers (Stork, 1996).

Among the various techniques for visual speech processing studied so far, Hidden Markov Model (HMM) holds the greatest promise due to its capabilities in modeling and analyzing temporal processes. In Goldschen’s system, HMM classifiers were explored for recognizing a closed set of TIMIT sentences based on speech sounds (Goldschen, 1994). In 1990, Welch et al explored audio-to-visual mapping using HMM for building speech-driven models. Silsbee and Bovik (1993) applied HMM to identify isolated words based on sounds alone. Tomlinson et al (1996) suggested a cross-product HMM topology, which allows asynchronous processing of visual signals and acoustic signals. Luettin et al (1996) used HMMs with an early integration strategy for both isolated digit recognition and connected digit recognition. In recent years, coupled HMM, product HMM and factorial HMM are explored for audio-visual integration (Zhang, 2002; Gravier, 2002; Nefian, 2002; Dupont, 2000).

Other studies relating to lip-reading include: the psychology of lip-reading (Dodd and Campbell, 1987); lip tracking by Yuille et al (1992), Coianiz et al (1996), Hennecke et al (1994), Eveno et al (2004), speaker identification by Cetingul et al (2006) and visual contribution to the perception of consonants by Binnie et al (1974).

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