Kanthashree Mysore Sathyendra — accepted AI-conference papers

2025

Compress, Gather, and Recompute: REFORMing Long-Context Processing in Transformers

Woomin Song, Sai Muralidhar Jayanthi, Srikanth Ronanki, Kanthashree Mysore Sathyendra, Jinwoo Shin, Aram Galstyan +2

NeurIPS 2025poster

As large language models increasingly gain popularity in real-world applications, processing extremely long contexts, often exceeding the model’s pre-trained context limits, has emerged as a critical challenge. While existing approaches to efficient long-context processing show promise, recurrent co…

Cited by 0 Source ↗Scholar ↗

2023

Dialog Act Guided Contextual Adapter for Personalized Speech Recognition

Feng-Ju Chang, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra, Kai Wei, Grant P. Strimel, Ross McGowan

ICASSP 2023accepted

Personalization in multi-turn dialogs has been a long standing challenge for end-to-end automatic speech recognition (E2E ASR) models. Recent work on contextual adapters has tackled rare word recognition using user catalogs. This adaptation, however, does not incorporate an important cue, the dialog…

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2023

Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition

Saumya Y. Sahai, Jing Liu, Thejaswi Muniyappa, Kanthashree Mysore Sathyendra, Anastasios Alexandridis, Grant P. Strimel +5

ICASSP 2023accepted

We present dual-attention neural biasing, an architecture designed to boost Wake Words (WW) recognition and improve inference time latency on speech recognition tasks. This architecture enables a dynamic switch for its runtime compute paths by exploiting WW spotting to select which branch of its att…

Cited by 6 Source ↗Scholar ↗

2023

Gated Contextual Adapters For Selective Contextual Biasing In Neural Transducers

Anastasios Alexandridis, Kanthashree Mysore Sathyendra, Grant P. Strimel, Feng-Ju Chang, Ariya Rastrow, Nathan Susanj +1

ICASSP 2023accepted

Neural contextual biasing for end-to-end neural ASR transducers has shown significant improvements in the recognition of named entities, such as contact names or device names. However, it comes with the cost of increased compute, as the biasing layers (which are usually based on cross-attention) add…

Cited by 12 Source ↗Scholar ↗

2023

Robust Acoustic And Semantic Contextual Biasing In Neural Transducers For Speech Recognition

Xuandi Fu, Kanthashree Mysore Sathyendra, Ankur Gandhe, Jing Liu, Grant P. Strimel, Ross McGowan +1

ICASSP 2023accepted

Attention-based contextual biasing approaches have shown significant improvements in the recognition of generic and/or personal rare-words in End-to-End Automatic Speech Recognition (E2E ASR) systems like neural transducers. These approaches employ crossattention to bias the model towards specific c…

Cited by 24 Source ↗Scholar ↗

2022

Contextual Adapters for Personalized Speech Recognition in Neural Transducers

Kanthashree Mysore Sathyendra, Thejaswi Muniyappa, Feng-Ju Chang, Jing Liu, Jinru Su, Grant P. Strimel +2

ICASSP 2022accepted

Personal rare word recognition in end-to-end Automatic Speech Recognition (E2E ASR) models is a challenge due to the lack of training data. A standard way to address this issue is with shallow fusion methods at inference time. However, due to their dependence on external language models and the dete…

Cited by 0 Source ↗Scholar ↗

2022

Multi-Task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding

Xuandi Fu, Feng-Ju Chang, Martin Radfar, Kai Wei, Jing Liu, Grant P. Strimel +1

ICASSP 2022accepted

End-to-end Spoken Language Understanding (E2E SLU) has attracted increasing interest due to its advantages of joint optimization and low latency when compared to traditionally cascaded pipelines. Existing E2E SLU models usually follow a two-stage configuration where an Automatic Speech Recognition (…

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2022

TINYS2I: A Small-Footprint Utterance Classification Model with Contextual Support for On-Device SLU

Anastasios Alexandridis, Kanthashree Mysore Sathyendra, Grant P. Strimel, Pavel Kveton, Jon Webb, Athanasios Mouchtaris

ICASSP 2022accepted

On-device spoken language understanding (SLU) offers the potential for significant latency savings compared to cloud-based processing, as the audio stream does not need to be transmitted to a server. We present Tiny Signal-to-interpretation (TinyS2I), an end-to-end on-device SLU approach which is fo…

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2020

Extreme Model Compression for On-device Natural Language Understanding

Kanthashree Mysore Sathyendra, Samridhi Choudhary, Leah Nicolich-Henkin

COLING 2020industry

In this paper, we propose and experiment with techniques for extreme compression of neural natural language understanding (NLU) models, making them suitable for execution on resource-constrained devices. We propose a task-aware, end-to-end compression approach that performs word-embedding compressio…

Cited by 11 Source ↗PDF ↗Scholar ↗

2020

Multilingual Grapheme-To-Phoneme Conversion with Byte Representation

Mingzhi Yu, Hieu Duy Nguyen, Alex Sokolov, Jack Lepird, Kanthashree Mysore Sathyendra, Samridhi Choudhary +2

ICASSP 2020accepted

Grapheme-to-phoneme (G2P) models convert a written word into its corresponding pronunciation and are essential components in automatic-speech-recognition and text-to-speech systems. Recently, the use of neural encoder-decoder architectures has substantially improved G2P accuracy for mono- and multi-…

Cited by 26 Source ↗Scholar ↗