Search Results for author: Ivan Bulyko

Found 20 papers, 1 papers with code

Investigating Training Strategies and Model Robustness of Low-Rank Adaptation for Language Modeling in Speech Recognition

no code implementations19 Jan 2024 Yu Yu, Chao-Han Huck Yang, Tuan Dinh, Sungho Ryu, Jari Kolehmainen, Roger Ren, Denis Filimonov, Prashanth G. Shivakumar, Ankur Gandhe, Ariya Rastow, Jia Xu, Ivan Bulyko, Andreas Stolcke

The use of low-rank adaptation (LoRA) with frozen pretrained language models (PLMs) has become increasing popular as a mainstream, resource-efficient modeling approach for memory-constrained hardware.

Language Modelling speech-recognition +1

Towards ASR Robust Spoken Language Understanding Through In-Context Learning With Word Confusion Networks

no code implementations5 Jan 2024 Kevin Everson, Yile Gu, Huck Yang, Prashanth Gurunath Shivakumar, Guan-Ting Lin, Jari Kolehmainen, Ivan Bulyko, Ankur Gandhe, Shalini Ghosh, Wael Hamza, Hung-Yi Lee, Ariya Rastrow, Andreas Stolcke

In the realm of spoken language understanding (SLU), numerous natural language understanding (NLU) methodologies have been adapted by supplying large language models (LLMs) with transcribed speech instead of conventional written text.

In-Context Learning intent-classification +6

Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue

no code implementations23 Dec 2023 Guan-Ting Lin, Prashanth Gurunath Shivakumar, Ankur Gandhe, Chao-Han Huck Yang, Yile Gu, Shalini Ghosh, Andreas Stolcke, Hung-Yi Lee, Ivan Bulyko

Specifically, our framework serializes tasks in the order of current paralinguistic attribute prediction, response paralinguistic attribute prediction, and response text generation with autoregressive conditioning.

Attribute Language Modelling +4

Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting

no code implementations27 Sep 2023 Chao-Han Huck Yang, Yile Gu, Yi-Chieh Liu, Shalini Ghosh, Ivan Bulyko, Andreas Stolcke

We explore the ability of large language models (LLMs) to act as speech recognition post-processors that perform rescoring and error correction.

Ranked #2 on Speech Recognition on WSJ eval92 (using extra training data)

In-Context Learning speech-recognition +1

Personalization for BERT-based Discriminative Speech Recognition Rescoring

no code implementations13 Jul 2023 Jari Kolehmainen, Yile Gu, Aditya Gourav, Prashanth Gurunath Shivakumar, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

On a test set with personalized named entities, we show that each of these approaches improves word error rate by over 10%, against a neural rescoring baseline.

speech-recognition Speech Recognition

Scaling Laws for Discriminative Speech Recognition Rescoring Models

no code implementations27 Jun 2023 Yile Gu, Prashanth Gurunath Shivakumar, Jari Kolehmainen, Ankur Gandhe, Ariya Rastrow, Ivan Bulyko

We study whether this scaling property is also applicable to second-pass rescoring, which is an important component of speech recognition systems.

speech-recognition Speech Recognition

PROCTER: PROnunciation-aware ConTextual adaptER for personalized speech recognition in neural transducers

no code implementations30 Mar 2023 Rahul Pandey, Roger Ren, Qi Luo, Jing Liu, Ariya Rastrow, Ankur Gandhe, Denis Filimonov, Grant Strimel, Andreas Stolcke, Ivan Bulyko

End-to-End (E2E) automatic speech recognition (ASR) systems used in voice assistants often have difficulties recognizing infrequent words personalized to the user, such as names and places.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

On-the-fly Text Retrieval for End-to-End ASR Adaptation

no code implementations20 Mar 2023 Bolaji Yusuf, Aditya Gourav, Ankur Gandhe, Ivan Bulyko

End-to-end speech recognition models are improved by incorporating external text sources, typically by fusion with an external language model.

Language Modelling Question Answering +4

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 Automatic Speech Recognition (ASR) +2

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 Sentence

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 Automatic Speech Recognition (ASR) +2

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 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 domain classification +3

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 Automatic Speech Recognition (ASR) +2

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 Automatic Speech Recognition (ASR) +3

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