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Found 13 papers, 2 papers with code
Im2Hands: Learning Attentive Implicit Representation of Interacting Two-Hand Shapes
To handle the shape complexity and interaction context between two hands, Im2Hands models the occupancy volume of two hands - conditioned on an RGB image and coarse 3D keypoints - by two novel attention-based modules responsible for (1) initial occupancy estimation and (2) context-aware occupancy refinement, respectively.
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
We address this by investigating synthetic audio data for audio-based DST.
Learning Monotonic Alignments with Source-Aware GMM Attention
Transformers with soft attention have been widely adopted to various sequence-to-sequence tasks.
Fast DCTTS: Efficient Deep Convolutional Text-to-Speech
We propose an end-to-end speech synthesizer, Fast DCTTS, that synthesizes speech in real time on a single CPU thread.
Facetron: A Multi-speaker Face-to-Speech Model based on Cross-modal Latent Representations
In this paper, we propose a multi-speaker face-to-speech waveform generation model that also works for unseen speaker conditions.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian
We propose a framework that can deform an object in a 2D image as it exists in 3D space.
SF-DST: Few-Shot Self-Feeding Reading Comprehension Dialogue State Tracking with Auxiliary Task
Few-shot dialogue state tracking (DST) model tracks user requests in dialogue with reliable accuracy even with a small amount of data.
Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
In dialogue state tracking (DST), labeling the dataset involves considerable human labor.
DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing
We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11).
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0
Specifically, we train an encoder module to map ECoG signals to latent embeddings that match Wav2Vec 2. 0 representations of the corresponding spoken speech.
Style Modeling for Multi-Speaker Articulation-to-Speech
In this paper, we propose a neural articulation-to-speech (ATS) framework that synthesizes high-quality speech from articulatory signal in a multi-speaker situation.
InterHandGen: Two-Hand Interaction Generation via Cascaded Reverse Diffusion
Sampling from our model yields plausible and diverse two-hand shapes in close interaction with or without an object.
