368 research outputs found
Lipreading with Long Short-Term Memory
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).Comment: Accepted for publication at ICASSP 201
Lip2AudSpec: Speech reconstruction from silent lip movements video
In this study, we propose a deep neural network for reconstructing
intelligible speech from silent lip movement videos. We use auditory
spectrogram as spectral representation of speech and its corresponding sound
generation method resulting in a more natural sounding reconstructed speech.
Our proposed network consists of an autoencoder to extract bottleneck features
from the auditory spectrogram which is then used as target to our main lip
reading network comprising of CNN, LSTM and fully connected layers. Our
experiments show that the autoencoder is able to reconstruct the original
auditory spectrogram with a 98% correlation and also improves the quality of
reconstructed speech from the main lip reading network. Our model, trained
jointly on different speakers is able to extract individual speaker
characteristics and gives promising results of reconstructing intelligible
speech with superior word recognition accuracy
Vid2speech: Speech Reconstruction from Silent Video
Speechreading is a notoriously difficult task for humans to perform. In this
paper we present an end-to-end model based on a convolutional neural network
(CNN) for generating an intelligible acoustic speech signal from silent video
frames of a speaking person. The proposed CNN generates sound features for each
frame based on its neighboring frames. Waveforms are then synthesized from the
learned speech features to produce intelligible speech. We show that by
leveraging the automatic feature learning capabilities of a CNN, we can obtain
state-of-the-art word intelligibility on the GRID dataset, and show promising
results for learning out-of-vocabulary (OOV) words.Comment: Accepted for publication at ICASSP 201
Visual units and confusion modelling for automatic lip-reading
Automatic lip-reading (ALR) is a challenging task because the visual speech signal is known to be missing some important information, such as voicing. We propose an approach to ALR that acknowledges that this information is missing but assumes that it is substituted or deleted in a systematic way that can be modelled. We describe a system that learns such a model and then incorporates it into decoding, which is realised as a cascade of weighted finite-state transducers. Our results show a small but statistically significant improvement in recognition accuracy. We also investigate the issue of suitable visual units for ALR, and show that visemes are sub-optimal, not but because they introduce lexical ambiguity, but because the reduction in modelling units entailed by their use reduces accuracy
Automatic Visual Speech Recognition
Intelligent SystemsElectrical Engineering, Mathematics and Computer Scienc
Lip-Reading with Visual Form Classification using Residual Networks and Bidirectional Gated Recurrent Units
Lip-reading is a method that focuses on the observation and interpretation of lip movements to understand spoken language. Previous studies have exclusively concentrated on a single variation of residual networks (ResNets). This study primarily aimed to conduct a comparative analysis of several types of ResNets. This study additionally calculates metrics for several word structures included in the GRID dataset, encompassing verbs, colors, prepositions, letters, and numerals. This component has not been previously investigated in other studies. The proposed approach encompasses several stages, namely pre-processing, which involves face detection and mouth location, feature extraction, and classification. The architecture for feature extraction comprises a 3-dimensional convolutional neural network (3D-CNN) integrated with ResNets. The management of temporal sequences during the classification phase is accomplished through the utilization of the bidirectional gated recurrent units (Bi-GRU) model. The experimental results demonstrated a character error rate (CER) of 14.09% and a word error rate (WER) of 28.51%. The combination of 3D-CNN ResNet-34 and Bi-GRU yielded superior outcomes in comparison to ResNet-18 and ResNet-50. The correlation between increased network depth and enhanced performance in lip-reading models was not consistently observed. Nevertheless, the incorporation of additional trained parameters offers certain benefits. Moreover, it has demonstrated superior levels of precision in comparison to human professionals in the task of distinguishing diverse word structures. Doi: 10.28991/HIJ-2023-04-02-010 Full Text: PD
Lip-Listening: Mixing Senses to Understand Lips using Cross Modality Knowledge Distillation for Word-Based Models
In this work, we propose a technique to transfer speech recognition
capabilities from audio speech recognition systems to visual speech
recognizers, where our goal is to utilize audio data during lipreading model
training. Impressive progress in the domain of speech recognition has been
exhibited by audio and audio-visual systems. Nevertheless, there is still much
to be explored with regards to visual speech recognition systems due to the
visual ambiguity of some phonemes. To this end, the development of visual
speech recognition models is crucial given the instability of audio models. The
main contributions of this work are i) building on recent state-of-the-art
word-based lipreading models by integrating sequence-level and frame-level
Knowledge Distillation (KD) to their systems; ii) leveraging audio data during
training visual models, a feat which has not been utilized in prior word-based
work; iii) proposing the Gaussian-shaped averaging in frame-level KD, as an
efficient technique that aids the model in distilling knowledge at the sequence
model encoder. This work proposes a novel and competitive architecture for
lip-reading, as we demonstrate a noticeable improvement in performance, setting
a new benchmark equals to 88.64% on the LRW dataset.Comment: arXiv admin note: text overlap with arXiv:2108.0354
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