Boris Ginsburg
NVIDIA applied research scientist; last-listed author on Parakeet speech recognition papers
Boris Ginsburg is an applied research scientist at NVIDIA whose work spans fast, scalable training algorithms for neural networks and speech recognition. Ginsburg is the last-listed author of the papers behind NVIDIA's Parakeet models, including the Token-and-Duration Transducer, the FastConformer encoder, the Parakeet-TDT-0.6B-v3 technical report and the unified streaming transducer used by parakeet-unified-en-0.6b.178910
Training at scale
NVIDIA's author page describes Ginsburg as an applied research scientist whose primary research is fast and scalable algorithms for training neural networks. Ginsburg co-authored "Large Batch Training of Convolutional Networks" (2017), which introduced layer-wise adaptive rate scaling, and "Mixed Precision Training" (2017).123
Ginsburg's NVIDIA technical blog posts cover mixed precision training for NLP and speech with OpenSeq2Seq (2018), pretraining BERT with layer-wise adaptive learning rates (2019), and building smaller speech recognition models with NVIDIA NeMo (2019).1
Speech recognition
Ginsburg co-authored the Jasper (2019) and QuartzNet (2019) convolutional acoustic models and the 2019 paper introducing NeMo, a toolkit for building AI applications from reusable neural modules with collections for speech recognition and language processing.465
Ginsburg is the last-listed author of "Efficient Sequence Transduction by Jointly Predicting Tokens and Durations" (2023) and "Fast Conformer with Linearly Scalable Attention" (2023), both cited by the Parakeet-TDT-0.6B-v3 model card, of the 2025 Canary-1B-v2 and Parakeet-TDT-0.6B-v3 technical report, and of the 2026 unified offline-streaming transducer paper cited by the parakeet-unified-en-0.6b model card.789101112
Work
NVIDIA parakeet-tdt-0.6b-v3Open weights
Last-listed author of the 2025 technical report introducing Canary-1B-v2 and Parakeet-TDT-0.6B-v3, which the model card points to for architecture and training details.911
NVIDIA parakeet-unified-en-0.6bOpen weights
Last-listed author of the 2026 paper on a unified offline and streaming transducer framework, the first reference in the parakeet-unified-en-0.6b model card.1012
NVIDIA ParakeetOpen weights
Last-listed author of the Token-and-Duration Transducer (TDT) and FastConformer papers, both cited as references in the Parakeet-TDT-0.6B-v3 model card.7811
Career timeline
Aug 2017
Co-authored "Large Batch Training of Convolutional Networks" with Yang You and Igor Gitman.2
Oct 2017
Co-authored the "Mixed Precision Training" paper.3
Apr 2019
Co-authored Jasper, an end-to-end convolutional neural acoustic model.4
Sep 2019
Co-authored the paper introducing the NeMo toolkit for building AI applications from neural modules.5
Oct 2019
Co-authored QuartzNet, an ASR model built from 1D time-channel separable convolutions.6
Apr 2023
Last-listed author of the Token-and-Duration Transducer (TDT) paper.7
May 2023
Last-listed author of the FastConformer paper on linearly scalable attention for speech recognition.8
Sep 2025
Last-listed author of the Canary-1B-v2 and Parakeet-TDT-0.6B-v3 technical report.9
Apr 2026
Last-listed author of the unified offline-streaming transducer paper behind parakeet-unified-en-0.6b.10
Related people
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- Aaron LefohnAlso at NVIDIA
- Ruoming PangTraining systems
- Azita MartinAlso at NVIDIA
- Yonghui WuTraining systems
- Bill DallyAlso at NVIDIA
- Abdelrahman MohamedSpeech and audio
- Bryan CatanzaroAlso at NVIDIA
- Adam PaszkeTraining systems
- Dan SuAlso at NVIDIA
- Akshita BhagiaTraining systems
- David LuebkeAlso at NVIDIA
- Alban DesmaisonTraining systems
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