Search Results for author: Ching-Feng Yeh

Found 20 papers, 3 papers with code

Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency

no code implementations5 Nov 2023 Sungho Jeon, Ching-Feng Yeh, Hakan Inan, Wei-Ning Hsu, Rashi Rungta, Yashar Mehdad, Daniel Bikel

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 transformer encoders.

Quantization

FLAP: Fast Language-Audio Pre-training

no code implementations2 Nov 2023 Ching-Feng Yeh, Po-Yao Huang, Vasu Sharma, Shang-Wen Li, Gargi Gosh

We propose Fast Language-Audio Pre-training (FLAP), a self-supervised approach that efficiently and effectively learns aligned audio and language representations through masking, contrastive learning and reconstruction.

AudioCaps Contrastive Learning +2

Efficient Speech Representation Learning with Low-Bit Quantization

no code implementations14 Dec 2022 Ching-Feng Yeh, Wei-Ning Hsu, Paden Tomasello, Abdelrahman Mohamed

With the development of hardware for machine learning, newer models often come at the cost of both increased sizes and computational complexity.

Model Compression Quantization +1

Dynamic Encoder Transducer: A Flexible Solution For Trading Off Accuracy For Latency

no code implementations5 Apr 2021 Yangyang Shi, Varun Nagaraja, Chunyang Wu, Jay Mahadeokar, Duc Le, Rohit Prabhavalkar, Alex Xiao, Ching-Feng Yeh, Julian Chan, Christian Fuegen, Ozlem Kalinli, Michael L. Seltzer

DET gets similar accuracy as a baseline model with better latency on a large in-house data set by assigning a lightweight encoder for the beginning part of one utterance and a full-size encoder for the rest.

speech-recognition Speech Recognition

Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR

no code implementations9 Nov 2020 Xiaohui Zhang, Frank Zhang, Chunxi Liu, Kjell Schubert, Julian Chan, Pradyot Prakash, Jun Liu, Ching-Feng Yeh, Fuchun Peng, Yatharth Saraf, Geoffrey Zweig

In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on three popular training criteria: LF-MMI, CTC and RNN-T.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Alignment Restricted Streaming Recurrent Neural Network Transducer

no code implementations5 Nov 2020 Jay Mahadeokar, Yuan Shangguan, Duc Le, Gil Keren, Hang Su, Thong Le, Ching-Feng Yeh, Christian Fuegen, Michael L. Seltzer

There is a growing interest in the speech community in developing Recurrent Neural Network Transducer (RNN-T) models for automatic speech recognition (ASR) applications.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

Streaming Attention-Based Models with Augmented Memory for End-to-End Speech Recognition

no code implementations3 Nov 2020 Ching-Feng Yeh, Yongqiang Wang, Yangyang Shi, Chunyang Wu, Frank Zhang, Julian Chan, Michael L. Seltzer

Attention-based models have been gaining popularity recently for their strong performance demonstrated in fields such as machine translation and automatic speech recognition.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Weak-Attention Suppression For Transformer Based Speech Recognition

no code implementations18 May 2020 Yangyang Shi, Yongqiang Wang, Chunyang Wu, Christian Fuegen, Frank Zhang, Duc Le, Ching-Feng Yeh, Michael L. Seltzer

Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR).

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

RNN-T For Latency Controlled ASR With Improved Beam Search

no code implementations5 Nov 2019 Mahaveer Jain, Kjell Schubert, Jay Mahadeokar, Ching-Feng Yeh, Kaustubh Kalgaonkar, Anuroop Sriram, Christian Fuegen, Michael L. Seltzer

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 model, language model, punctuation model, inverse text normalization) into one single model.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Domain Adversarial Training for Accented Speech Recognition

no code implementations7 Jun 2018 Sining Sun, Ching-Feng Yeh, Mei-Yuh Hwang, Mari Ostendorf, Lei Xie

In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem.

Accented Speech Recognition Multi-Task Learning +1

Training Augmentation with Adversarial Examples for Robust Speech Recognition

no code implementations7 Jun 2018 Sining Sun, Ching-Feng Yeh, Mari Ostendorf, Mei-Yuh Hwang, Lei Xie

This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models.

Data Augmentation Robust Speech Recognition +1

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