Inter-Frame Contextual Modelling For Visual Speech Recognition

In this paper, we present a new approach to visual speech recognition which improves contextual modelling by combining Inter- Frame Dependent and Hidden Markov Models. This approach captures contextual information in visual speech that may be lost using a Hidden Markov Model alone. We apply contextual modelling to a large speaker independent isolated digit recognition task, and compare our approach to two commonly adopted feature based techniques for incorporating speech dynamics. Results are presented from baseline feature based systems and the combined modelling technique. We illustrate that both of these techniques achieve similar levels of performance when used independently. However significant improvements in performance can be achieved through a combination of the two. In particular we report an improvement in excess of 17% relative Word Error Rate in comparison to our best baseline system. © 2010 IEEE

Tandem approach for information fusion in audio visual speech recognition

Speech is the most frequently preferred medium for humans to interact with their environment making it an ideal instrument for human-computer interfaces. However, for the speech recognition systems to be more prevalent in real life applications, high recognition accuracy together with speaker independency and robustness to hostile conditions is necessary. One of the main preoccupation for speech recognition systems is acoustic noise. Audio Visual Speech Recognition systems intend to overcome the noise problem utilizing visual speech information generally extracted from the face or in particular the lip region. Visual speech information is known to be a complementary source for speech perception and is not impacted by acoustic noise. This advantage brings in two additional issues into the task which are visual feature extraction and information fusion. There is extensive research on both issues but an admissable level of success has not been reached yet. This work concentrates on the issue of information fusion and proposes a novel methodology. The aim of the proposed technique is to deploy a preliminary decision stage at frame level as an initial stage and feed the Hidden Markov Model with the output posterior probabilities as in tandem HMM approach. First, classification is performed for each modality separately. Sequentially, the individual classifiers of each modality are combined to obtain posterior probability vectors corresponding to each speech frame. The purpose of using a preliminary stage is to integrate acoustic and visual data for maximum class separability. Hidden Markov Models are employed as the second stage of modelling because of their ability to handle temporal evolutions of data. The proposed approach is investigated in a speaker independent scenario for digit recognition with the existence of diverse levels of car noise. The method is compared with a principal information fusion framework in audio visual speech recognition which is Multiple Stream Hidden Markov Models (MSHMM). The results on M2VTS database show that the novel method achieves resembling performance with less processing time as compared to MSHMM

Hierarchical Discriminant Features for Audio-Visual LVCSR

We propose the use of a hierarchical, two-stage discriminant transformation for obtaining audio-visual features that improve automatic speech recognition. Linear discriminant analysis (LDA), followed by a maximum likelihood linear transform (MLLT) is first applied on MFCC based audio-only features, as well as on visualonly features, obtained by a discrete cosine transform of the video region of interest. Subsequently, a second stage of LDA and MLLT is applied on the concatenation of the resulting single modality features. The obtained audio-visual features are used to train a traditional HMM based speech recognizer. Experiments on the IBM ViaVoice TM audio-visual database demonstrate that the proposed feature fusion method improves speaker-independent, large vocabulary, continuous speech recognition for both clean and noisy audio conditions considered. A 24% relative word error rate reduction over an audio-only system is achieved in the latter case

Reconhecimento automático de fala com processamento simultâneo de características acústicas e visuais

Tese de mestrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200