5 citations · 15 across the 6 of their papers we have counts for
7 papers
FC-TTS: Style and Timbre Control in Zero-Shot Text-to-Speech with Disentangled Speech Representations
Yoonhyung Lee, Hyunsin Park, Jinhwan Park +1
Recent advances in zero-shot text-to-speech (TTS) have enabled accurate imitation of reference speech in terms of both speaking style and speaker timbre. However, achieving disenta…
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…
Macro-block dropout for improved regularization in training end-to-end speech recognition models
Chanwoo Kim, Sathish Indurti, Jinhwan Park +1
This paper proposes a new regularization algorithm referred to as macro-block dropout. The overfitting issue has been a difficult problem in training large neural network models. T…
S-SGD: Symmetrical Stochastic Gradient Descent with Weight Noise Injection for Reaching Flat Minima
Wonyong Sung, Iksoo Choi, Jinhwan Park +2
The stochastic gradient descent (SGD) method is most widely used for deep neural network (DNN) training. However, the method does not always converge to a flat minimum of the loss…
Single Stream Parallelization of Recurrent Neural Networks for Low Power and Fast Inference
Wonyong Sung, Jinhwan Park
As neural network algorithms show high performance in many applications, their efficient inference on mobile and embedded systems are of great interests. When a single stream recur…
FPGA-Based Low-Power Speech Recognition with Recurrent Neural Networks
Minjae Lee, Kyuyeon Hwang, Jinhwan Park +3
In this paper, a neural network based real-time speech recognition (SR) system is developed using an FPGA for very low-power operation. The implemented system employs two recurrent…
FPGA Based Implementation of Deep Neural Networks Using On-chip Memory Only
Jinhwan Park, Wonyong Sung
Deep neural networks (DNNs) demand a very large amount of computation and weight storage, and thus efficient implementation using special purpose hardware is highly desired. In thi…