We present a Lipreading system, i.e. a speech recognition system using only visual features, which uses domain-adversarial training for speaker independence. Domain-adversarial training is integrated into the optimization of a lipreader based on a stack of feedforward and LSTM (Long Short-Term Memory) recurrent neural networks, yielding an end-to-end trainable system which only requires a very small number of frames of untranscribed target data to substantially improve the recognition accuracy on the target speaker. On pairs of different source and target speakers, we achieve a relative accuracy improvement of around 40 with only 15 to 20 seconds of untranscribed target speech data. On multi-speaker training setups, the accuracy improvements are smaller but still substantial.
Versatile lipreading features have been proposed, such as Active Appearance Models @cite_17 , Local Binary Patterns @cite_4 , and PCA-based @cite_31 and @cite_36 . For tackling speaker dependency, diverse scaling and normalization techniques have been employed @cite_5 @cite_29 . Classification is often done with Hidden Markov Models (HMMs), @cite_16 @cite_21 @cite_12 @cite_7 . Mouth tracking is done as a preprocessing step @cite_7 @cite_21 @cite_39 . For a comprehensive review see @cite_37 .
@cite_37 @cite_4 @cite_7 @cite_36 @cite_29 @cite_21 @cite_39 @cite_5 @cite_31 @cite_16 @cite_12 @cite_17
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Visual speech information plays an important role in lipreading under noisy conditions or for listeners with a hearing impairment. In this paper, we present local spatiotemporal descriptors to represent and recognize spoken isolated phrases based solely on visual input. Spatiotemporal local binary patterns extracted from mouth regions are used for describing isolated phrase sequences. In our experiments with 817 sequences from ten phrases and 20 speakers, promising accuracies of 62 and 70 were obtained in speaker-independent and speaker-dependent recognition, respectively. In comparison with other methods on AVLetters database, the accuracy, 62.8 , of our method clearly outperforms the others. Analysis of the confusion matrix for 26 English letters shows the good clustering characteristics of visemes for the proposed descriptors. The advantages of our approach include local processing and robustness to monotonic gray-scale changes. Moreover, no error prone segmentation of moving lips is needed. For automatic lipreading, there are many competing methods for feature extraction. Often, because of the complexity of the task these methods are tested on only quite restricted datasets, such as the letters of the alphabet or digits, and from only a few speakers. In this paper we compare some of the leading methods for lip feature extraction and compare them on the GRID dataset which uses a constrained vocabulary over, in this case, 15 speakers. Previously the GRID data has had restricted attention because of the requirements to track the face and lips accurately. We overcome this via the use of a novel linear predictor (LP) tracker which we use to control an Active Appearance Model (AAM). By ignoring shape and or appearance parameters from the AAM we can quantify the effect of appearance and or shape when lip-reading. We find that shape alone is a useful cue for lipreading (which is consistent with human experiments). However, the incremental effect of shape on appearance appears to be not significant which implies that the inner appearance of the mouth contains more information than the shape. The article compares two approaches to the description of ultrasound vocal tract images for application in a "silent speech interface," one based on tongue contour modeling, and a second, global coding approach in which images are projected onto a feature space of Eigentongues. A curvature-based lip profile feature extraction method is also presented. Extracted visual features are input to a neural network which learns the relation between the vocal tract configuration and line spectrum frequencies (LSF) contained in a one-hour speech corpus. An examination of the quality of LSFs derived from the two approaches demonstrates that the Eigemongues approach has a more efficient implementation and provides superior results based on a normalized mean squared error criterion. We improve the performance of a hybrid connectionist speech recognition system by incorporating visual information about the corresponding lip movements. Specifically, we investigate the benefits of adding visual features in the presence of additive noise and crosstalk (cocktail party effect). Our study extends our previous experiments by using a new visual front end, and an alternative architecture for combining the visual and acoustic information. Furthermore, we have extended our recognizer to a multi-speaker, connected letters recognizer. Our results show a significant improvement for the combined architecture (acoustic and visual information) over just the acoustic system in the presence of additive noise and crosstalk. > The multimodal nature of speech is often ignored in human-computer interaction, but lip deformations and other body motion, such as those of the head, convey additional information. We integrate speech cues from many sources and this improves intelligibility, especially when the acoustic signal is degraded. The paper shows how this additional, often complementary, visual speech information can be used for speech recognition. Three methods for parameterizing lip image sequences for recognition using hidden Markov models are compared. Two of these are top-down approaches that fit a model of the inner and outer lip contours and derive lipreading features from a principal component analysis of shape or shape and appearance, respectively. The third, bottom-up, method uses a nonlinear scale-space analysis to form features directly from the pixel intensity. All methods are compared on a multitalker visual speech recognition task of isolated letters.