5 accepted papers
This paper reports on a novel invasive brain–computer interface (BCI) paradigm that has successfully reconstructed spoken sentences from invasive electrocorticogram (ECoG) signals using deep-neural-network-based encoders and a pre-trained neural vocoder. We recorded ECoG signals while 13 participant…
"We propose an action-conditional human motion generation method using variational implicit neural representations (INR). The variational formalism enables action-conditional distributions of INRs, from which one can easily sample representations to generate novel human motion sequences. Our method…
Invasive brain–machine interfaces (BMIs) are a promising neurotechnological venture for achieving direct speech communication from a human brain, but it faces many challenges. In this paper, we measured the invasive electrocorticogram (ECoG) signals from seven participating epilepsy patients as they…
We investigate the feasibility of sequence-level knowledge distillation of Sequence-to-Sequence (Seq2Seq) models for Large Vocabulary Continuous Speech Recognition (LVCSR). We first use a pre-trained larger teacher model to generate multiple hypotheses per utterance with beam search. With the same i…
For speech recognition in noisy environments, we propose a multi-task autoencoder which estimates not only clean speech features but also noise features from noisy speech. We introduce the deSpeeching autoencoder, which excludes speech signals from noisy speech, and combine it with the conventional…