Review of Lip-Reading Recognition

Zheng, Guoli; Zhu, Meili; Li, Feng

doi:10.1109/iscid.2014.110

Cited by 5 publications

(3 citation statements)

References 45 publications

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“…[1][2][3] These techniques facilitate communications and have the potential to replace human interpreters [4] in medical service applications. Lip-reading also provides an effective human-machine interface that uses natural speech for interactions with smart machines, [5,6] including robotics, [7] prosthetics, [8] computers, [9] and even augmented reality (AR)/virtual reality (VR) environments. [10,11] Silent speech interfaces, where no acoustic sound is produced, are often in demand when privacy or a quiet environment is desired, such as in public areas or hospitals.…”

Section: Introductionmentioning

confidence: 99%

Electromyogram‐Based Lip‐Reading via Unobtrusive Dry Electrodes and Machine Learning Methods

Dong

Song

et al. 2023

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The Stony Brook University provided guidelines for study procedures. Informed consent was obtained from all participants. After data acquisition, the data were segmented into every single trial, and features were calculated. LDA/SVM training programs were applied to get the classification results. Figures 5 and 6 and Figures S14-S21, Supporting Information summarize the recognition results of all four subjects. The results of 11 words from different viseme groups using the LDA and the SVM were 94.8% ± 0.035 and 90.5% ± 0.061, respectively. The results for nine pairs of similar words were 86.8% ± 0.053 and 82.6% ± 0.056 when using the LDA and the SVM, respectively. The recognition accuracy was represented as mean ± standard derivation. Matlab was used as the software for the statistical analysis.

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Section: Introductionmentioning

confidence: 99%

Electromyogram‐Based Lip‐Reading via Unobtrusive Dry Electrodes and Machine Learning Methods

Dong

Song

et al. 2023

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“…Studies on this subject include many fields such as artificial intelligence, information engineering, and image processing systems. With the rapid developments in automatic speech recognition, human-computer interaction, and computer vision technologies, lip-reading applications are used not only to improve speech recognition but also in new areas such as human-machine and anti-terrorism [11].…”

Section: Introductionmentioning

confidence: 99%

New Feature Extraction Approaches Based on Spatial Points for Visual-Only Lip-Reading

Tung¹,

Tekin²

2022

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The act of speaking takes place as a result of the joint use of both the senses of vision and hearing. The visual senses of the event of speech play an important role in lip-reading, especially when the sound is distorted or inaccessible. Visual-only-based lip-reading is a more difficult problem than audio-image-based lip-reading problems. In this study, three new spatial feature approaches to visual-only lip-reading are presented. To test the proposed feature extraction approaches, three datasets named AVLetters2 consisting of letters, AVDigits consisting of digits, and AVLetAVDig consisting of a combination of these two were used. First of all, the facial elements and lips were separated and the lip borders were marked with 20 points. Then, based on these spatial points, feature vectors were obtained with the feature approaches named Symmetric Euclidean Distance (SED), Central Euclidean Distance (CED), and Triple Points Angles (TPA). Extracted feature vectors were given to the CNN-LSTM network and 26 characters and 10 digits were tried to be estimated. As a result of the findings, the best success results for AVLetters2, AVDigits, and AVLetAVDig datasets were obtained by the SED+CNN+LSTM method as 53.2, 81.6, 59.8, respectively. When compared with the studies in the literature on the same data set, it was seen that very high and successful results were obtained.

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“…Количество используемых виземных классов зависит от языка, и для русского обычно использова-лось от 10 до 14 классов [7][8][9]. В наших экспериментах мы использовали от 2 (разделение на гласные и согласные) до 48 виземных классов (по количеству фонем), с шагом 2.…”

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Accuracy increase for automatic visual Russian speech recognition: viseme classes optimization

Викторович¹,

Валерьевич²,

Анатольевич³

2018

Naučno-teh. vestn. inf. tehnol. meh. opt.

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Научно-технический вестник информационных технологий, механики и оптики, Mechanics and Optics, 2018, vol. 18, no. 2, pp. 346-349 (in Russian). doi: 10.17586/2226-1494-2018 Abstract Nowadays there are a lot of continuous studies on the correct viseme classes to be used for the most effective automatic lipreading. The paper proposes a structured approach for the development of speaker-dependent classes of visemes. This method

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Review of Lip-Reading Recognition

Cited by 5 publications

References 45 publications

Electromyogram‐Based Lip‐Reading via Unobtrusive Dry Electrodes and Machine Learning Methods

Electromyogram‐Based Lip‐Reading via Unobtrusive Dry Electrodes and Machine Learning Methods

New Feature Extraction Approaches Based on Spatial Points for Visual-Only Lip-Reading

Accuracy increase for automatic visual Russian speech recognition: viseme classes optimization

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