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Found 7 papers, 2 papers with code
Diff2Lip: Audio Conditioned Diffusion Models for Lip-Synchronization
We show results on both reconstruction (same audio-video inputs) as well as cross (different audio-video inputs) settings on Voxceleb2 and LRW datasets.
SIDGAN: High-Resolution Dubbed Video Generation via Shift-Invariant Learning
Dubbed video generation aims to accurately synchronize mouth movements of a given facial video with driving audio while preserving identity and scene-specific visual dynamics, such as head pose and lighting.
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains creating a need to adapt to new domains with small memory and deployment overhead.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Prompt-tuning in ASR systems for efficient domain-adaptation
In this work, we overcome the problem using prompt-tuning, a methodology that trains a small number of domain token embedding parameters to prime a transformer-based LM to a particular domain.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Continual Entity Learning in Language Models for Conversational Agents
Neural language models (LM) trained on diverse corpora are known to work well on previously seen entities, however, updating these models with dynamically changing entities such as place names, song titles and shopping items requires re-training from scratch and collecting full sentences containing these entities.
Neural Composition: Learning to Generate from Multiple Models
Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and biasing of some components to the user's personal preferences.
Jasper: An End-to-End Convolutional Neural Acoustic Model
In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data.
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Speech Recognition
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