44 citations · 91 across the 7 of their papers we have counts for
8 papers
Region-Aware Masking for Accent-Robust Cross-Lingual Text-to-Speech
Haoqi Li, Shivam Mehta, Ravi Teja Gadde +1
Accent leakage remains a critical challenge in cross-lingual zero-shot text-to-speech (TTS), where models inadvertently transfer both the speaker's timbre and source-language accen…
Diff2Lip: Audio Conditioned Diffusion Models for Lip-Synchronization
Soumik Mukhopadhyay, Saksham Suri, Ravi Teja Gadde +1
The task of lip synchronization (lip-sync) seeks to match the lips of human faces with different audio. It has various applications in the film industry as well as for creating vir…
Prompt Tuning GPT-2 language model for parameter-efficient domain adaptation of ASR systems
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3
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 memor…
Prompt-tuning in ASR systems for efficient domain-adaptation
Saket Dingliwal, Ashish Shenoy, Sravan Bodapati +3
Automatic Speech Recognition (ASR) systems have found their use in numerous industrial applications in very diverse domains. Since domain-specific systems perform better than their…
Towards Continual Entity Learning in Language Models for Conversational Agents
Ravi Teja Gadde, Ivan Bulyko
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…
Neural Composition: Learning to Generate from Multiple Models
Denis Filimonov, Ravi Teja Gadde, Ariya Rastrow
Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and…
Jasper: An End-to-End Convolutional Neural Acoustic Model
Jason Li, Vitaly Lavrukhin, Boris Ginsburg +5
In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data. Our model, Jasper, uses only 1D conv…
Training Neural Speech Recognition Systems with Synthetic Speech Augmentation
Jason Li, Ravi Gadde, Boris Ginsburg +1
Building an accurate automatic speech recognition (ASR) system requires a large dataset that contains many hours of labeled speech samples produced by a diverse set of speakers. Th…