Topic
Viseme
About: Viseme is a research topic. Over the lifetime, 865 publications have been published within this topic receiving 17889 citations.
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21 Jan 2023
TL;DR: In this article , the authors presented a large-scale audio-visual dataset for Persian, which consists of almost 220 hours of videos with 1760 corresponding speakers, which is suitable for automatic speech recognition, audio visual speech recognition and speaker recognition.
Abstract: In recent years, significant progress has been made in automatic lip reading. But these methods require large-scale datasets that do not exist for many low-resource languages. In this paper, we have presented a new multipurpose audio-visual dataset for Persian. This dataset consists of almost 220 hours of videos with 1760 corresponding speakers. In addition to lip reading, the dataset is suitable for automatic speech recognition, audio-visual speech recognition, and speaker recognition. Also, it is the first large-scale lip reading dataset in Persian. A baseline method was provided for each mentioned task. In addition, we have proposed a technique to detect visemes (a visual equivalent of a phoneme) in Persian. The visemes obtained by this method increase the accuracy of the lip reading task by 7% relatively compared to the previously proposed visemes, which can be applied to other languages as well.
01 Aug 1999
TL;DR: This paper presents an automatic vowel recognition system which can perform in quasi real-time and focus mainly on the recognition of 5 different German vowels and their corresponding visemes (images).
Abstract: The performance of speech recognition systems decreases dramatically in noisy environments. A robust human-computer interaction system should therefore make use of both, acoustic and visual signals. In this paper we present an automatic vowel recognition system which can perform in quasi real-time. We focus mainly on the recognition of 5 different German vowels (a, e, i, o, u) and their corresponding visemes (images). First the position of the continuously moving face is determined. The speech parameters of the spoken vowel along with the model parameters of the lip's image are fed to a neural network to recognize the uttered vowel. The face tracking is shape-independent and hence no special requirements concerning the color or shape of the face are needed.
TL;DR: The recognition rate of a speech recognition system is improved by compensating for changes in the user's speech that result from factors such as emotion, anxiety or fatigue.
Abstract: The recognition rate of a speech recognition system is improved by compensating for changes in the user's speech that result from factors such as emotion, anxiety or fatigue. A speech signal derived from a user's utterance is modified by a preprocessor 32 and provided to a speech recognition system to improve the recognition rate. The speech signal is modified based on a bio-signal which is indicative of the user's emotional state.
TL;DR: The paper shows the HMM which describing the dynamic of speech, coupled with the combined feature for describing the global and local texture is the best model.
Abstract: This paper aims to give a solutions for the construction of chinese visual speech feature model based on HMM. We propose and discuss three kind representation model of the visual speech which are lip geometrical features, lip motion features and lip texture features. The model combines the advantages of the local LBP and global DCT texture information together, which shows better performance than the single feature. Equally the model combines the advantages of the local LBP and geometrical information together is better than single feature. By computing the recognition rate of the visemes from the model, the paper shows the HMM which describing the dynamic of speech, coupled with the combined feature for describing the global and local texture is the best model.
20 Oct 2004
TL;DR: A dynamic visual feature extraction scheme to capture important lip motion information for visual speech recognition and shows that the proposed high discriminative dynamic features, when augmented to the static, yields superior recognition performance.
Abstract: This paper presents a dynamic visual feature extraction scheme to capture important lip motion information for visual speech recognition Discriminative projections based on a-priori chosen speech classes, phonemes and visemes, are applied to the concatenation of pre-extracted static visual features First- and second-order temporal derivatives are subsequently extracted to further represent the dynamic differences Experiments on a connected digits task demonstrate that the proposed high discriminative dynamic features, when augmented to the static, yields superior recognition performance Compared to the commonly used delta and acceleration features, the proposed dynamic feature leads to an 8% absolute improvement in terms of word accuracy for the considered recognition task