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Found 11 papers, 1 papers with code
Investigating the dynamics of hand and lips in French Cued Speech using attention mechanisms and CTC-based decoding
Along with the release of this dataset, a benchmark will be reported for word-level recognition, a novelty in the automatic recognition of French CS.
BERT, can HE predict contrastive focus? Predicting and controlling prominence in neural TTS using a language model
We collect a corpus of utterances containing contrastive focus and we evaluate the accuracy of a BERT model, finetuned to predict quantized acoustic prominence features, on these samples.
Self-supervised speech unit discovery from articulatory and acoustic features using VQ-VAE
The human perception system is often assumed to recruit motor knowledge when processing auditory speech inputs.
Multistream neural architectures for cued-speech recognition using a pre-trained visual feature extractor and constrained CTC decoding
This paper proposes a simple and effective approach for automatic recognition of Cued Speech (CS), a visual communication tool that helps people with hearing impairment to understand spoken language with the help of hand gestures that can uniquely identify the uttered phonemes in complement to lipreading.
Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation
We propose a computational model of speech production combining a pre-trained neural articulatory synthesizer able to reproduce complex speech stimuli from a limited set of interpretable articulatory parameters, a DNN-based internal forward model predicting the sensory consequences of articulatory commands, and an internal inverse model based on a recurrent neural network recovering articulatory commands from the acoustic speech input.
Learning robust speech representation with an articulatory-regularized variational autoencoder
It is increasingly considered that human speech perception and production both rely on articulatory representations.
Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
The prosody of a spoken word is determined by its surrounding context.
What the Future Brings: Investigating the Impact of Lookahead for Incremental Neural TTS
In this paper, we study the behavior of a neural sequence-to-sequence TTS system when used in an incremental mode, i. e. when generating speech output for token n, the system has access to n + k tokens from the text sequence.
Dynamical Variational Autoencoders: A Comprehensive Review
Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and corresponding latent vectors, relying on recurrent neural networks or state-space models.
Autoencoders for music sound modeling: a comparison of linear, shallow, deep, recurrent and variational models
This study investigates the use of non-linear unsupervised dimensionality reduction techniques to compress a music dataset into a low-dimensional representation which can be used in turn for the synthesis of new sounds.
Audio and Speech Processing Sound
