Lipreading, i.e. speech recognition from visual-only recordings of a speaker's face, can be achieved with a processing pipeline based solely on neural networks, yielding significantly better accuracy than conventional methods. Feed-forward and recurrent neural network layers (namely Long Short-Term Memory; LSTM) are stacked to form a single structure which is trained by back-propagating error gradients through all the layers. The performance of such a stacked network was experimentally evaluated and compared to a standard Support Vector Machine classifier using conventional computer vision features (Eigenlips and Histograms of Oriented Gradients). The evaluation was performed on data from 19 speakers of the publicly available GRID corpus. With 51 different words to classify, we report a best word accuracy on held-out evaluation speakers of 79.6 using the end-to-end neural network-based solution (11.6 improvement over the best feature-based solution evaluated).
Lipreading has been used as a complementary modality for speech recognition from noisy audio data @cite_27 @cite_29 , as well as for purely visual speech recognition @cite_26 @cite_25 @cite_35 . The latter gives rise to a , which is defined as a system enabling speech communication to take place when an audible acoustic signal is unavailable'' @cite_5 . Silent Speech technology has a large number of applications: It allows persons with certain speech impairments ( laryngectomees, whose voice box ( larynx ) has been removed) to communicate, as well as enabling confidential and undisturbing communication in public places @cite_5 . Further uses of lipreading have been proposed, automatic speech extraction from surveillance videos and its interpretation for forensic purposes @cite_22 . Lipreading has been augmented with images of the tongue and vocal tract @cite_19 @cite_34 @cite_12 . Furthermore, there are Silent Speech interfaces based on very different principles, like speech recognition from electromyography @cite_20 @cite_21 @cite_8 @cite_33 or (electro-)magnetic articulography @cite_31 .
@cite_35 @cite_26 @cite_22 @cite_33 @cite_8 @cite_29 @cite_21 @cite_19 @cite_27 @cite_5 @cite_31 @cite_34 @cite_20 @cite_25 @cite_12
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Automatic recognition of the acoustic speech signal alone is inaccurate and computationally expensive. Additional sources of speech information, such as lipreading (or speechreading), should enhance automatic speech recognition, just as lipreading is used by humans to enhance speech recognition when the acoustic signal is degraded. This paper describes an automatic lipreading system which has been developed. A commercial device performs the acoustic speech recognition independently of the lipreading system. The recognition domain is restricted to isolated utterances and speaker dependent recognition. The speaker faces a solid state camera which sends digitized video to a minicomputer system with custom video processing hardware. The video data is sampled during an utterance and then reduced to a template consisting of visual speech parameter time sequences. The distances between the incoming template and all of the trained templates for each utterance in the vocabulary are computed and a visual recognition candidate is obtained. The combination of the acoustic and visual recognition candidates is shown to yield a final recognition accuracy which greatly exceeds the acoustic recognition accuracy alone. Practical considerations and the possible enhancement of speaker independent and continuous speech recognition systems are also discussed. Machine lipreading is a special type of automatic speech recognition (ASR) which transcribes human speech by visually interpreting the movement of related face regions including lips, face, and tongue. Recently, deep neural network based lipreading methods show great potential and have exceeded the accuracy of experienced human lipreaders in some benchmark datasets. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data. In this paper, we propose LCANet, an end-to-end deep neural network based lipreading system. LCANet encodes input video frames using a stacked 3D convolutional neural network (CNN), highway network and bidirectional GRU network. The encoder effectively captures both short-term and long-term spatio-temporal information. More importantly, LCANet incorporates a cascaded attention-CTC decoder to generate output texts. By cascading CTC with attention, it partially eliminates the defect of the conditional independence assumption of CTC within the hidden neural layers, and this yields notably performance improvement as well as faster convergence. The experimental results show the proposed system achieves a 1.3 CER and 3.0 WER on the GRID corpus database, leading to a 12.3 improvement compared to the state-of-the-art methods. This paper describes a speech recognition system that uses both acoustic and visual speech information to improve recognition performance in noisy environments. The system consists of three components: a visual module; an acoustic module; and a sensor fusion module. The visual module locates and tracks the lip movements of a given speaker and extracts relevant speech features. This task is performed with an appearance-based lip model that is learned from example images. Visual speech features are represented by contour information of the lips and grey-level information of the mouth area. The acoustic module extracts noise-robust features from the audio signal. Finally the sensor fusion module is responsible for the joint temporal modeling of the acoustic and visual feature streams and is realized using multistream hidden Markov models (HMMs). The multistream method allows the definition of different temporal topologies and levels of stream integration and hence enables the modeling of temporal dependencies more accurately than traditional approaches. We present two different methods to learn the asynchrony between the two modalities and how to incorporate them in the multistream models. The superior performance for the proposed system is demonstrated on a large multispeaker database of continuously spoken digits. On a recognition task at 15 dB acoustic signal-to-noise ratio (SNR), acoustic perceptual linear prediction (PLP) features lead to 56 error rate, noise robust RASTA-PLP (relative spectra) acoustic features to 7.2 error rate and combined noise robust acoustic features and visual features to 2.5 error rate. This paper presents preliminary experiments using the Kaldi toolkit to investigate audiovisual speech recognition (AVSR) in noisy environments using deep neural networks (DNNs). In particular we use a single-speaker large vocabulary, continuous audiovisual speech corpus to compare the performance of visual-only, audio-only and audiovisual speech recognition. The models trained using the Kaldi toolkit are compared with the performance of models trained using conventional hidden Markov models (HMMs). In addition, we compare the performance of a speech recognizer both with and without visual features over nine different SNR levels of babble noise ranging from 20dB down to -20dB. The results show that the DNN outperforms conventional HMMs in all experimental conditions, especially for the lip-reading only system, which achieves a gain of 37.19 accuracy (84.67 absolute word accuracy). Moreover, the DNN provides an effective improvement of 10 and 12dB SNR respectively for both the single modal and bimodal speech recognition systems. However, integrating the visual features using simple feature fusion is only effective in SNRs at 5dB and above. Below this the degradion in accuracy of an audiovisual system is similar to the audio only recognizer. Index Terms: lip-reading, speech reading, audiovisual speech recognition