11 citations · 24 across the 18 of their papers we have counts for
12 papers · 1 filter
TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems
Vladimir Bataev, Lilit Grigoryan, Andrei Andrusenko +3
Contextualization is essential for production automatic speech recognition (ASR) systems, where user-provided phrases must be recognized accurately under strict latency constraints…
Reducing the Offline-Streaming Gap for Unified ASR Transducer with Consistency Regularization
Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan +3
Unification of automatic speech recognition (ASR) systems reduces development and maintenance costs, but training a single model to perform well in both offline and low-latency str…
FlexCTC: GPU-powered CTC Beam Decoding With Advanced Contextual Abilities
Lilit Grigoryan, Vladimir Bataev, Nikolay Karpov +3
While beam search improves speech recognition quality over greedy decoding, standard implementations are slow, often sequential, and CPU-bound. To fully leverage modern hardware ca…
TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree
Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan +2
Recognizing specific key phrases is an essential task for contextualized Automatic Speech Recognition (ASR). However, most existing context-biasing approaches have limitations asso…
Pushing the Limits of Beam Search Decoding for Transducer-based ASR models
Lilit Grigoryan, Vladimir Bataev, Andrei Andrusenko +3
Transducer models have emerged as a promising choice for end-to-end ASR systems, offering a balanced trade-off between recognition accuracy, streaming capabilities, and inference s…
NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding
Vladimir Bataev, Andrei Andrusenko, Lilit Grigoryan +3
Statistical n-gram language models are widely used for context-biasing tasks in Automatic Speech Recognition (ASR). However, existing implementations lack computational efficiency…
RNN-Transducer-based Losses for Speech Recognition on Noisy Targets
Vladimir Bataev
Training speech recognition systems on noisy transcripts is a significant challenge in industrial pipelines, where datasets are enormous and ensuring accurate transcription for eve…
TTS-Transducer: End-to-End Speech Synthesis with Neural Transducer
Vladimir Bataev, Subhankar Ghosh, Vitaly Lavrukhin +1
This work introduces TTS-Transducer - a novel architecture for text-to-speech, leveraging the strengths of audio codec models and neural transducers. Transducers, renowned for thei…
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter
Andrei Andrusenko, Aleksandr Laptev, Vladimir Bataev +2
Accurate recognition of rare and new words remains a pressing problem for contextualized Automatic Speech Recognition (ASR) systems. Most context-biasing methods involve modificati…
Label-Looping: Highly Efficient Decoding for Transducers
Vladimir Bataev, Hainan Xu, Daniel Galvez +2
This paper introduces a highly efficient greedy decoding algorithm for Transducer-based speech recognition models. We redesign the standard nested-loop design for RNN-T decoding, s…
Powerful and Extensible WFST Framework for RNN-Transducer Losses
Aleksandr Laptev, Vladimir Bataev, Igor Gitman +1
This paper presents a framework based on Weighted Finite-State Transducers (WFST) to simplify the development of modifications for RNN-Transducer (RNN-T) loss. Existing implementat…
Text-only domain adaptation for end-to-end ASR using integrated text-to-mel-spectrogram generator
Vladimir Bataev, Roman Korostik, Evgeny Shabalin +2
We propose an end-to-end Automatic Speech Recognition (ASR) system that can be trained on transcribed speech data, text-only data, or a mixture of both. The proposed model uses an…