198 citations · 276 across the 15 of their papers we have counts for
15 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…
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…
Unified Semi-Supervised Pipeline for Automatic Speech Recognition
Nune Tadevosyan, Nikolay Karpov, Andrei Andrusenko +2
Automatic Speech Recognition has been a longstanding research area, with substantial efforts dedicated to integrating semi-supervised learning due to the scarcity of labeled datase…
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…
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…
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation
Zhehuai Chen, He Huang, Andrei Andrusenko +6
We present a novel Speech Augmented Language Model (SALM) with {\em multitask} and {\em in-context} learning capabilities. SALM comprises a frozen text LLM, a audio encoder, a moda…
Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition
Andrei Andrusenko, Rauf Nasretdinov, Aleksei Romanenko
Optimization of modern ASR architectures is among the highest priority tasks since it saves many computational resources for model training and inference. The work proposes a new U…
LT-LM: a novel non-autoregressive language model for single-shot lattice rescoring
Anton Mitrofanov, Mariya Korenevskaya, Ivan Podluzhny +7
Neural network-based language models are commonly used in rescoring approaches to improve the quality of modern automatic speech recognition (ASR) systems. Most of the existing met…
Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition
Aleksandr Laptev, Andrei Andrusenko, Ivan Podluzhny +3
With the rapid development of speech assistants, adapting server-intended automatic speech recognition (ASR) solutions to a direct device has become crucial. Researchers and indust…
Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset
Andrei Andrusenko, Aleksandr Laptev, Ivan Medennikov
This paper presents an exploration of end-to-end automatic speech recognition systems (ASR) for the largest open-source Russian language data set -- OpenSTT. We evaluate different…
Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario
Ivan Medennikov, Maxim Korenevsky, Tatiana Prisyach +9
Speaker diarization for real-life scenarios is an extremely challenging problem. Widely used clustering-based diarization approaches perform rather poorly in such conditions, mainl…
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation
Aleksandr Laptev, Roman Korostik, Aleksey Svischev +3
Data augmentation is one of the most effective ways to make end-to-end automatic speech recognition (ASR) perform close to the conventional hybrid approach, especially when dealing…
Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription
Andrei Andrusenko, Aleksandr Laptev, Ivan Medennikov
While end-to-end ASR systems have proven competitive with the conventional hybrid approach, they are prone to accuracy degradation when it comes to noisy and low-resource condition…