Search Results for author: Sungsoo Kim

Found 6 papers, 1 papers with code

Two-Pass End-to-End ASR Model Compression

no code implementations8 Jan 2022 Nauman Dawalatabad, Tushar Vatsal, Ashutosh Gupta, Sungsoo Kim, Shatrughan Singh, Dhananjaya Gowda, Chanwoo Kim

With the use of popular transducer-based models, it has become possible to practically deploy streaming speech recognition models on small devices [1].

Knowledge Distillation Model Compression +2

A review of on-device fully neural end-to-end automatic speech recognition algorithms

no code implementations14 Dec 2020 Chanwoo Kim, Dhananjaya Gowda, Dongsoo Lee, Jiyeon Kim, Ankur Kumar, Sungsoo Kim, Abhinav Garg, Changwoo Han

Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder based on a Weighted Finite State Transducer (WFST), and so on.

Automatic Speech Recognition speech-recognition

Attention based on-device streaming speech recognition with large speech corpus

no code implementations2 Jan 2020 Kwangyoun Kim, Kyungmin Lee, Dhananjaya Gowda, Junmo Park, Sungsoo Kim, Sichen Jin, Young-Yoon Lee, Jinsu Yeo, Daehyun Kim, Seokyeong Jung, Jungin Lee, Myoungji Han, Chanwoo Kim

In this paper, we present a new on-device automatic speech recognition (ASR) system based on monotonic chunk-wise attention (MoChA) models trained with large (> 10K hours) corpus.

Automatic Speech Recognition Data Augmentation +2

end-to-end training of a large vocabulary end-to-end speech recognition system

no code implementations22 Dec 2019 Chanwoo Kim, Sungsoo Kim, Kwangyoun Kim, Mehul Kumar, Jiyeon Kim, Kyungmin Lee, Changwoo Han, Abhinav Garg, Eunhyang Kim, Minkyoo Shin, Shatrughan Singh, Larry Heck, Dhananjaya Gowda

Our end-to-end speech recognition system built using this training infrastructure showed a 2. 44 % WER on test-clean of the LibriSpeech test set after applying shallow fusion with a Transformer language model (LM).

Data Augmentation speech-recognition +1

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