11 papers
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…
Chunk-wise Attention Transducers for Fast and Accurate Streaming Speech-to-Text
Hainan Xu, Vladimir Bataev, Travis M. Bartley +1
We propose Chunk-wise Attention Transducer (CHAT), a novel extension to RNN-T models that processes audio in fixed-size chunks while employing cross-attention within each chunk. Th…
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…