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Found 11 papers, 6 papers with code
Self-Supervised Models of Speech Infer Universal Articulatory Kinematics
Self-Supervised Learning (SSL) based models of speech have shown remarkable performance on a range of downstream tasks.
SD-HuBERT: Sentence-Level Self-Distillation Induces Syllabic Organization in HuBERT
Data-driven unit discovery in self-supervised learning (SSL) of speech has embarked on a new era of spoken language processing.
CiwaGAN: Articulatory information exchange
This paper introduces CiwaGAN, a model of human spoken language acquisition that combines unsupervised articulatory modeling with an unsupervised model of information exchange through the auditory modality.
Neural Latent Aligner: Cross-trial Alignment for Learning Representations of Complex, Naturalistic Neural Data
The proposed framework learns more cross-trial consistent representations than the baselines, and when visualized, the manifold reveals shared neural trajectories across trials.
Deep Speech Synthesis from MRI-Based Articulatory Representations
Finally, through a series of ablations, we show that the proposed MRI representation is more comprehensive than EMA and identify the most suitable MRI feature subset for articulatory synthesis.
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interpretable space.
Articulatory Representation Learning Via Joint Factor Analysis and Neural Matrix Factorization
In this work, we propose a novel articulatory representation decomposition algorithm that takes the advantage of guided factor analysis to derive the articulatory-specific factors and factor scores.
A Fast and Accurate Pitch Estimation Algorithm Based on the Pseudo Wigner-Ville Distribution
Estimation of fundamental frequency (F0) in voiced segments of speech signals, also known as pitch tracking, plays a crucial role in pitch synchronous speech analysis, speech synthesis, and speech manipulation.
Evidence of Vocal Tract Articulation in Self-Supervised Learning of Speech
Recent self-supervised learning (SSL) models have proven to learn rich representations of speech, which can readily be utilized by diverse downstream tasks.
Deep Speech Synthesis from Articulatory Representations
In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract.
Brain2Char: A Deep Architecture for Decoding Text from Brain Recordings
These results set a new state-of-the-art on decoding text from brain and demonstrate the potential of Brain2Char as a high-performance communication BCI.
