Decoding human speech from neural signals is essential for brain-computer interface (BCI) technologies that aim to restore speech in populations with neurological deficits. However, it remains a highly challenging task, compounded by the scarce availability of neural signals with corresponding speech, ...
Home Neural Computing and Applications Article Decoding silent speech: a machine learning perspective on data, methods, and frameworksReview Open access Published: 20 February 2025 Volume 37, pages 6995–7013, (2025) Cite this article Download PDF You have full access to this open access article ...
Distinctions in evoked neural activity between silent- and overt-speech attempts The spelling system was controlled by silent-speech attempts, differing from our previous work in which the same participant used overt-speech attempts (attempts to speak aloud) to control a similar speech-decoding syste...
NVIDIA NeMo Framework is a scalable and cloud-native generative AI framework built for researchers and PyTorch developers working on Large Language Models (LLMs), Multimodal Models (MMs), Automatic Speech Recognition (ASR), Text to Speech (TTS), and Computer Vision (CV) domains. It is designed...
An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets
Seq2SeqSharp is a tensor based fast & flexible deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, different network types (Transformer, LSTM, BiLSTM and so on), multi-GPUs supported, cross-platforms (Windows, Linux, x86, ...
Section 3 gives an overview of the experimental framework, EEG data acquisition during playing the android games, and the results obtained from the experiments are presented in Section 4 Offline and online analysis of image and EEG data, Performance evaluation of the proposed FT2FDNN. The results...
1990s.Jürgen Schmidhuber and Sepp Hochreiter, both computer scientists from Germany, proposed the long short-term memory recurrent neural network framework in 1997. 2000s.Hinton and his colleagues at the University of Toronto pioneered restricted Boltzmann machines, a sort of generative artificial neura...
ofneural networkarchitecture that excels at processing sequential data, most prominently associated withlarge language models (LLMs). Transformer models have also achieved elite performance in other fields ofartificial intelligence (AI), such as computer vision, speech recognition and time series ...
^H. Schroter, A. N. Escalante-B, T. Rosenkranz, and A. Maier, “Deepfilternet: A low complexity speech enhancement framework for full-band audio based on deep filtering,” in ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022, pp...