You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Speechreading is the task of inferring phonetic information from visually observed articulatory facial movements, and 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 and natural-sounding acoustic speech signal from silent video frames of a speaking person. We train our model on speakers from the GRID and TCD-TIMIT datasets, and evaluate the quality and intelligibility of reconstructed speech using common objective measurements. We show that speech predictions from the proposed model attain scores which indicate significantly improved quality over existing models. In addition, we show promising results towards reconstructing speech from an unconstrained dictionary.

Cited Abstract(s):
[1]: Three neural network models were trained on the forward mapping from articulatory positions to acoustic outputs for a single speaker of the Edinburgh multi-channel articulatory speech database. The model parameters (i.e., connection weights) were learned via the backpropagation of error signals generated by the difference between acoustic outputs of the models, and their acoustic targets. Efficacy of the trained models was assessed by subjecting the models’ acoustic outputs to speech intelligibility tests. The results of these tests showed that enough phonetic information was captured by the models to support rates of word identification as high as 84 , approaching an identification rate of 92 for the actual target stimuli. These forward models could serve as one component of a data-driven articulatory synthesizer. The models also provide the first step toward building a model of spoken word acquisition and phonological development trained on real speech.

[2]: This paper is concerned with generating intelligible audio speech from a video of a person talking. Regression and classification methods are proposed first to estimate static spectral envelope features from active appearance model visual features. Two further methods are then developed to incorporate temporal information into the prediction: A feature-level method using multiple frames and a model-level method based on recurrent neural networks. Speech excitation information is not available from the visual signal, so methods to artificially generate aperiodicity and fundamental frequency are developed. These are combined within the STRAIGHT vocoder to produce a speech signal. The various systems are optimized through objective tests before applying subjective intelligibility tests that determine a word accuracy of 85 from a set of human listeners on the GRID audio–visual speech database. This compares favorably with a previous regression-based system that serves as a baseline, which achieved a word accuracy of 33 .

[3]: Conversion of silent articulation captured by ultrasound and video to modal speech.Comparison of GMM and full-covariance phonetic HMM without vocabulary limitation.HMM-based approach allows the use of linguistic information for regularization.Objective evaluation showed a lower but more fluctuant spectral distortion for HMM.Perceptual evaluation showed a better intelligibility for HMM on consonants. This article investigates the use of statistical mapping techniques for the conversion of articulatory movements into audible speech with no restriction on the vocabulary, in the context of a silent speech interface driven by ultrasound and video imaging. As a baseline, we first evaluated the GMM-based mapping considering dynamic features, proposed by (2007) for voice conversion. Then, we proposed a 'phonetically-informed' version of this technique, based on full-covariance HMM. This approach aims (1) at modeling explicitly the articulatory timing for each phonetic class, and (2) at exploiting linguistic knowledge to regularize the problem of silent speech conversion. Both techniques were compared on continuous speech, for two French speakers (one male, one female). For modal speech, the HMM-based technique showed a lower spectral distortion (objective evaluation). However, perceptual tests (transcription and XAB discrimination tests) showed a better intelligibility of the GMM-based technique, probably related to its less fluctuant quality. For silent speech, a perceptual identification test revealed a better segmental intelligibility for the HMM-based technique on consonants.
The second approach, and the one used in this work, is to model speechreading as a visual-to-acoustic mapping problem in which the label" of each short video segment is a corresponding feature vector representing the audio signal. Kello and Plaut [1] and Hueber and Bailly [3] attempted this approach using various sensors to record mouth movements, whereas Cornu and Milner [2] used active appearance model (AAM) visual features as input to a recurrent neural network.