12 citations · 49 across the 17 of their papers we have counts for
17 papers
Physics Informed Distillation for Diffusion Models
Joshua Tian Jin Tee, Kang Zhang, Hee Suk Yoon +3
Diffusion models have recently emerged as a potent tool in generative modeling. However, their inherent iterative nature often results in sluggish image generation due to the requi…
Data-driven grapheme-to-phoneme representations for a lexicon-free text-to-speech
Abhinav Garg, Jiyeon Kim, Sushil Khyalia +2
Grapheme-to-Phoneme (G2P) is an essential first step in any modern, high-quality Text-to-Speech (TTS) system. Most of the current G2P systems rely on carefully hand-crafted lexicon…
On the compression of shallow non-causal ASR models using knowledge distillation and tied-and-reduced decoder for low-latency on-device speech recognition
Nagaraj Adiga, Jinhwan Park, Chintigari Shiva Kumar +4
Recently, the cascaded two-pass architecture has emerged as a strong contender for on-device automatic speech recognition (ASR). A cascade of causal and shallow non-causal encoders…
Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals
Dhananjaya Gowda, Sudarsana Reddy Kadiri, Brad Story +1
In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Conventional f…
Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
Paavo Alku, Sudarsana Reddy Kadiri, Dhananjaya Gowda
In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven ma…
Mitigating the Exposure Bias in Sentence-Level Grapheme-to-Phoneme (G2P) Transduction
Eunseop Yoon, Hee Suk Yoon, Dhananjaya Gowda +7
Text-to-Text Transfer Transformer (T5) has recently been considered for the Grapheme-to-Phoneme (G2P) transduction. As a follow-up, a tokenizer-free byte-level model based on T5 re…
Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition
Jash Rathod, Nauman Dawalatabad, Shatrughan Singh +1
The smaller memory bandwidth in smart devices prompts development of smaller Automatic Speech Recognition (ASR) models. To obtain a smaller model, one can employ the model compress…
Two-Pass End-to-End ASR Model Compression
Nauman Dawalatabad, Tushar Vatsal, Ashutosh Gupta +4
Speech recognition on smart devices is challenging owing to the small memory footprint. Hence small size ASR models are desirable. With the use of popular transducer-based models,…
Formant Tracking Using Quasi-Closed Phase Forward-Backward Linear Prediction Analysis and Deep Neural Networks
Dhananjaya Gowda, Bajibabu Bollepalli, Sudarsana Reddy Kadiri +1
Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation method…
Semi-supervised transfer learning for language expansion of end-to-end speech recognition models to low-resource languages
Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda +2
In this paper, we propose a three-stage training methodology to improve the speech recognition accuracy of low-resource languages. We explore and propose an effective combination o…
A comparison of streaming models and data augmentation methods for robust speech recognition
Jiyeon Kim, Mehul Kumar, Dhananjaya Gowda +2
In this paper, we present a comparative study on the robustness of two different online streaming speech recognition models: Monotonic Chunkwise Attention (MoChA) and Recurrent Neu…
Streaming end-to-end speech recognition with jointly trained neural feature enhancement
Chanwoo Kim, Abhinav Garg, Dhananjaya Gowda +2
In this paper, we present a streaming end-to-end speech recognition model based on Monotonic Chunkwise Attention (MoCha) jointly trained with enhancement layers. Even though the Mo…
A review of on-device fully neural end-to-end automatic speech recognition algorithms
Chanwoo Kim, Dhananjaya Gowda, Dongsoo Lee +5
In this paper, we review various end-to-end automatic speech recognition algorithms and their optimization techniques for on-device applications. Conventional speech recognition sy…
Attention based on-device streaming speech recognition with large speech corpus
Kwangyoun Kim, Kyungmin Lee, Dhananjaya Gowda +10
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.…
Improved Multi-Stage Training of Online Attention-based Encoder-Decoder Models
Abhinav Garg, Dhananjaya Gowda, Ankur Kumar +3
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. A three-stage traini…
power-law nonlinearity with maximally uniform distribution criterion for improved neural network training in automatic speech recognition
Chanwoo Kim, Mehul Kumar, Kwangyoun Kim +1
In this paper, we describe the Maximum Uniformity of Distribution (MUD) algorithm with the power-law nonlinearity. In this approach, we hypothesize that neural network training wil…
end-to-end training of a large vocabulary end-to-end speech recognition system
Chanwoo Kim, Sungsoo Kim, Kwangyoun Kim +10
In this paper, we present an end-to-end training framework for building state-of-the-art end-to-end speech recognition systems. Our training system utilizes a cluster of Central Pr…