Feature-based natural language processing for GSL synthesis

The work reported in this study is based on research that has been carried out while developing a sign synthesis system for Greek Sign Language (GSL) and involves theoretical linguistic analysis as well as lexicon and grammar resources derived from this analysis. We focus on the organisation of linguistic knowledge that initiates the multi-functional processing required to achieve sign generation performed by a virtual signer. In this context, structure rules and lexical coding support sign synthesis of GSL utterances, by exploitation of avatar technologies for the representation of the linguistic message. Sign generation involves two subsystems: a Greek-to-GSL conversion subsystem and a sign performance subsystem. The conversion subsystem matches input strings of written Greek-to-GSL structure patterns, exploiting Natural Language Processing (NLP) mechanisms. The sign performance subsystem uses parsed output of GSL structure patterns, enriched with sign-specific information, to activate a virtual signer for the performance of properly coded linguistic messages. Both the conversion and the synthesis procedure are based on adequately constructed electronic linguistic resources. Applicability of sign synthesis is demonstrated with the example of a Web-based prototype environment for GSL grammar teaching. © John Benjamins Publishing Company

On the use of a decimative spectral estimation method based on eigenanalysis and SVD for formant and bandwidth tracking of speech signals

In this paper, a Decimative Spectral estimation method based on Eigenanalysis and SVD (Singular Value Decomposition) is presented and applied to speech signals in order to estimate Formant/Bandwidth values. The underlying model decomposes a signal into complex damped sinusoids. The algorithm is applied not only on speech samples but on a small amount of the autocorrelation coefficients of a speech frame as well, for finer estimation. Correct estimation of Formant/Bandwidth values depend on the model order thus, the requested number of poles. Overall, experimentation results indicate that the proposed methodology successfully estimates formant trajectories and their respective bandwidths

Formant estimation of speech signals using subspace-based spectral analysis

The objective of this paper is to propose a signal processing scheme that employs subspace-based spectral analysis for the purpose of formant estimation of speech signals. Specifically, the scheme is based on decimative spectral estimation that uses Eigenanalysis and SVD (Singular Value Decomposition). The underlying model assumes a decomposition of the processed signal into complex damped sinusoids. In the case of formant tracking, the algorithm is applied on a small amount of the autocorrelation coefficients of a speech frame. The proposed scheme is evaluated on both artificial and real speech utterances from the TIMIT database. For the first case, comparative results to standard methods are provided which indicate that the proposed methodology successfully estimates formant trajectories

Beyond the manual channel: Proceedings of the 6th Workshop on the Representation and Processing of Sign Languages: Beyond the Manual Channel

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SL-ReDu: Greek sign language recognition for educational applications. Project description and early results

We present SL-ReDu, a recently commenced innovative project that aims to exploit deep-learning progress to advance the state-of-the-art in video-based automatic recognition of Greek Sign Language (GSL), while focusing on the use-case of GSL education as a second language. We first briefly overview the project goals, focal areas, and timeline. We then present our initial deep learning-based approach for GSL recognition that employs efficient visual tracking of the signer hands, convolutional neural networks for feature extraction, and attention-based encoder-decoder sequence modeling for sign prediction. Finally, we report experimental results for small-vocabulary, isolated GSL recognition on the single-signer "Polytropon" corpus. To our knowledge, this work constitutes the first application of deep-learning techniques to GSL. © 2020 ACM