3 papers
eess.AS2025
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…
eess.AS2025
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…
cs.LG2025
WIND: Accelerated RNN-T Decoding with Windowed Inference for Non-blank Detection
Hainan Xu, Vladimir Bataev, Lilit Grigoryan +1
We propose Windowed Inference for Non-blank Detection (WIND), a novel strategy that significantly accelerates RNN-T inference without compromising model accuracy. During model infe…