Search Results for author: Ivan Bulyko

Found 9 papers, 1 papers with code

A Likelihood Ratio based Domain Adaptation Method for E2E Models

no code implementations10 Jan 2022 Chhavi Choudhury, Ankur Gandhe, Xiaohan Ding, Ivan Bulyko

In this work, we explore a contextual biasing approach using likelihood-ratio that leverages text data sources to adapt RNN-T model to new domains and entities.

Automatic Speech Recognition Domain Adaptation

Towards Continual Entity Learning in Language Models for Conversational Agents

no code implementations30 Jul 2021 Ravi Teja Gadde, Ivan Bulyko

Neural language models (LM) trained on diverse corpora are known to work well on previously seen entities, however, updating these models with dynamically changing entities such as place names, song titles and shopping items requires re-training from scratch and collecting full sentences containing these entities.

Language Modelling

Attention-based Contextual Language Model Adaptation for Speech Recognition

1 code implementation Findings (ACL) 2021 Richard Diehl Martinez, Scott Novotney, Ivan Bulyko, Ariya Rastrow, Andreas Stolcke, Ankur Gandhe

When applied to a large de-identified dataset of utterances collected by a popular voice assistant platform, our method reduces perplexity by 7. 0% relative over a standard LM that does not incorporate contextual information.

Automatic Speech Recognition voice assistant

Personalization Strategies for End-to-End Speech Recognition Systems

no code implementations15 Feb 2021 Aditya Gourav, Linda Liu, Ankur Gandhe, Yile Gu, Guitang Lan, Xiangyang Huang, Shashank Kalmane, Gautam Tiwari, Denis Filimonov, Ariya Rastrow, Andreas Stolcke, Ivan Bulyko

We also describe a novel second-pass de-biasing approach: used in conjunction with a first-pass shallow fusion that optimizes on oracle WER, we can achieve an additional 14% improvement on personalized content recognition, and even improve accuracy for the general use case by up to 2. 5%.

Speech Recognition

Domain-aware Neural Language Models for Speech Recognition

no code implementations5 Jan 2021 Linda Liu, Yile Gu, Aditya Gourav, Ankur Gandhe, Shashank Kalmane, Denis Filimonov, Ariya Rastrow, Ivan Bulyko

As voice assistants become more ubiquitous, they are increasingly expected to support and perform well on a wide variety of use-cases across different domains.

Domain Adaptation Speech Recognition

Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion

no code implementations30 Nov 2020 Vijay Ravi, Yile Gu, Ankur Gandhe, Ariya Rastrow, Linda Liu, Denis Filimonov, Scott Novotney, Ivan Bulyko

We show that this simple method can improve performance on rare words by 3. 7% WER relative without degradation on general test set, and the improvement from USF is additive to any additional language model based rescoring.

Automatic Speech Recognition

Multi-task Language Modeling for Improving Speech Recognition of Rare Words

no code implementations23 Nov 2020 Chao-Han Huck Yang, Linda Liu, Ankur Gandhe, Yile Gu, Anirudh Raju, Denis Filimonov, Ivan Bulyko

We show that our rescoring model trained with these additional tasks outperforms the baseline rescoring model, trained with only the language modeling task, by 1. 4% on a general test and by 2. 6% on a rare word test set in terms of word-error-rate relative (WERR).

Automatic Speech Recognition Multi-Task Learning

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