9 citations · 14 across the 8 of their papers we have counts for
8 papers
Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Yonggan Fu, Lexington Whalen, Abhinav Garg +23
We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR…
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Abhinav Garg, Jiyeon Kim, Sushil Khyalia +2
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely on carefully hand-crafted lexicon…
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages
Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda +2
In this paper, we propose a three-stage training methodology to improve the speech recognition accuracy of low-resource languages. We explore and propose an effective combination o…
A comparison of streaming models and data augmentation methods for robust speech recognition
Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda +2
In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neu…
Streaming end-to-end speech recognition with jointly trained neural feature enhancement
Chanwoo Kim, Abhinav Garg, Dhananjaya Gowda +2
In this paper, we present a streaming end-to-end speech recognition model based on Monotonic Chunkwise Attention (MoCha) jointly trained with enhancement layers. Even though the Mo…
A review of on-device fully neural end-to-end automatic speech recognition algorithms
Chanwoo Kim, Dhananjaya Gowda, Dongsoo Lee +5
In this paper, we review various end-to-end automatic speech recognition algorithms and their optimization techniques for on-device applications. Conventional speech recognition sy…
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
Abhinav Garg, Dhananjaya Gowda, Ankur Kumar +3
In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models. A three-stage traini…
end-to-end training of a large vocabulary end-to-end speech recognition system
Chanwoo Kim, Sungsoo Kim, Kwangyoun Kim +10
In this paper, we present an end-to-end training framework for building state-of-the-art end-to-end speech recognition systems. Our training system utilizes a cluster of Central Pr…