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Found 6 papers, 3 papers with code
Unconstrained Dysfluency Modeling for Dysfluent Speech Transcription and Detection
Dysfluent speech modeling requires time-accurate and silence-aware transcription at both the word-level and phonetic-level.
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.
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.
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.
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.
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.
