Search Results for author: Mehul Kumar

Found 5 papers, 0 papers with code

A comparison of streaming models and data augmentation methods for robust speech recognition

no code implementations19 Nov 2021 Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda, Abhinav Garg, Chanwoo Kim

However, we observe that training of MoChA models seems to be more sensitive to various factors such as the characteristics of training sets and the incorporation of additional augmentations techniques.

Data Augmentation Robust Speech Recognition +1

Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages

no code implementations19 Nov 2021 Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda, Abhinav Garg, Chanwoo Kim

To improve the accuracy of a low-resource Italian ASR, we leverage a well-trained English model, unlabeled text corpus, and unlabeled audio corpus using transfer learning, TTS augmentation, and SSL respectively.

Data Augmentation speech-recognition +2

Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models

no code implementations28 Dec 2019 Abhinav Garg, Dhananjaya Gowda, Ankur Kumar, Kwangyoun Kim, Mehul Kumar, Chanwoo Kim

In this paper, we propose a refined multi-stage multi-task training strategy to improve the performance of online attention-based encoder-decoder (AED) models.

Language Modelling Multi-Task Learning

power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition

no code implementations22 Dec 2019 Chanwoo Kim, Mehul Kumar, Kwangyoun Kim, Dhananjaya Gowda

With the power function-based MUD, we apply a power-function based nonlinearity where power function coefficients are chosen to maximize the likelihood assuming that nonlinearity outputs follow the uniform distribution.

Automatic Speech Recognition speech-recognition

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 Language Modelling +2

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