66 citations · 181 across the 18 of their papers we have counts for
19 papers
Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency
Sungho Jeon, Ching-Feng Yeh, Hakan Inan +4
In this paper, we show that a simple self-supervised pre-trained audio model can achieve comparable inference efficiency to more complicated pre-trained models with speech transfor…
Efficient Speech Representation Learning with Low-Bit Quantization
Ching-Feng Yeh, Wei-Ning Hsu, Paden Tomasello +1
With the development of hardware for machine learning, newer models often come at the cost of both increased sizes and computational complexity. In effort to improve the efficiency…
Continual Learning for On-Device Speech Recognition using Disentangled Conformers
Anuj Diwan, Ching-Feng Yeh, Wei-Ning Hsu +4
Automatic speech recognition research focuses on training and evaluating on static datasets. Yet, as speech models are increasingly deployed on personal devices, such models encoun…
SUPERB @ SLT 2022: Challenge on Generalization and Efficiency of Self-Supervised Speech Representation Learning
Tzu-hsun Feng, Annie Dong, Ching-Feng Yeh +11
We present the SUPERB challenge at SLT 2022, which aims at learning self-supervised speech representation for better performance, generalization, and efficiency. The challenge buil…
TorchAudio: Building Blocks for Audio and Speech Processing
Yao-Yuan Yang, Moto Hira, Zhaoheng Ni +20
This document describes version 0.10 of TorchAudio: building blocks for machine learning applications in the audio and speech processing domain. The objective of TorchAudio is to a…
Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency
Yangyang Shi, Varun Nagaraja, Chunyang Wu +9
We propose a dynamic encoder transducer (DET) for on-device speech recognition. One DET model scales to multiple devices with different computation capacities without retraining or…
Semantic Distance: A New Metric for ASR Performance Analysis Towards Spoken Language Understanding
Suyoun Kim, Abhinav Arora, Duc Le +4
Word Error Rate (WER) has been the predominant metric used to evaluate the performance of automatic speech recognition (ASR) systems. However, WER is sometimes not a good indicator…
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
Xiaohui Zhang, Frank Zhang, Chunxi Liu +8
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on thre…
Alignment Restricted Streaming Recurrent Neural Network Transducer
Jay Mahadeokar, Yuan Shangguan, Duc Le +6
There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications. RNN-T is t…
Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition
Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi +4
Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition. One m…
Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications
Yongqiang Wang, Yangyang Shi, Frank Zhang +4
In this paper, we summarize the application of transformer and its streamable variant, Emformer based acoustic model for large scale speech recognition applications. We compare the…
Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
This paper proposes an efficient memory transformer Emformer for low latency streaming speech recognition. In Emformer, the long-range history context is distilled into an augmente…
Weak-Attention Suppression For Transformer Based Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR). However, adjacent acousti…
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
Chunyang Wu, Yongqiang Wang, Yangyang Shi +2
Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and sequence-to-sequence speech recogni-tion. However, it requires access to the full sequence, and…
AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition
Yi-Chen Chen, Zhaojun Yang, Ching-Feng Yeh +2
As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems. In this paper, our goal is to build a unifi…
RNN-T For Latency Controlled ASR With Improved Beam Search
Mahaveer Jain, Kjell Schubert, Jay Mahadeokar +5
Neural transducer-based systems such as RNN Transducers (RNN-T) for automatic speech recognition (ASR) blend the individual components of a traditional hybrid ASR systems (acoustic…
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention
Ching-Feng Yeh, Jay Mahadeokar, Kaustubh Kalgaonkar +6
We explore options to use Transformer networks in neural transducer for end-to-end speech recognition. Transformer networks use self-attention for sequence modeling and comes with…
Training Augmentation with Adversarial Examples for Robust Speech Recognition
Sining Sun, Ching-Feng Yeh, Mari Ostendorf +2
This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast…
Domain Adversarial Training for Accented Speech Recognition
Sining Sun, Ching-Feng Yeh, Mei-Yuh Hwang +2
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source…