17 citations · 34 across the 15 of their papers we have counts for
15 papers
LV-CTC: Non-autoregressive ASR with CTC and latent variable models
Yuya Fujita, Shinji Watanabe, Xuankai Chang +1
Non-autoregressive (NAR) models for automatic speech recognition (ASR) aim to achieve high accuracy and fast inference by simplifying the autoregressive (AR) generation process of…
HuBERTopic: Enhancing Semantic Representation of HuBERT through Self-supervision Utilizing Topic Model
Takashi Maekaku, Jiatong Shi, Xuankai Chang +2
Recently, the usefulness of self-supervised representation learning (SSRL) methods has been confirmed in various downstream tasks. Many of these models, as exemplified by HuBERT an…
Cross-Modal Multi-Tasking for Speech-to-Text Translation via Hard Parameter Sharing
Brian Yan, Xuankai Chang, Antonios Anastasopoulos +2
Recent works in end-to-end speech-to-text translation (ST) have proposed multi-tasking methods with soft parameter sharing which leverage machine translation (MT) data via secondar…
Exploring Speech Recognition, Translation, and Understanding with Discrete Speech Units: A Comparative Study
Xuankai Chang, Brian Yan, Kwanghee Choi +14
Speech signals, typically sampled at rates in the tens of thousands per second, contain redundancies, evoking inefficiencies in sequence modeling. High-dimensional speech features…
Exploration of Efficient End-to-End ASR using Discretized Input from Self-Supervised Learning
Xuankai Chang, Brian Yan, Yuya Fujita +2
Self-supervised learning (SSL) of speech has shown impressive results in speech-related tasks, particularly in automatic speech recognition (ASR). While most methods employ the out…
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation
Motoi Omachi, Brian Yan, Siddharth Dalmia +2
The black-box nature of end-to-end speech translation (E2E ST) systems makes it difficult to understand how source language inputs are being mapped to the target language. To solve…
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation
Xuankai Chang, Takashi Maekaku, Yuya Fujita +1
This work presents our end-to-end (E2E) automatic speech recognition (ASR) model targetting at robust speech recognition, called Integraded speech Recognition with enhanced speech…
A Comparative Study on Non-Autoregressive Modelings for Speech-to-Text Generation
Yosuke Higuchi, Nanxin Chen, Yuya Fujita +6
Non-autoregressive (NAR) models simultaneously generate multiple outputs in a sequence, which significantly reduces the inference speed at the cost of accuracy drop compared to aut…
Streaming End-to-End ASR based on Blockwise Non-Autoregressive Models
Tianzi Wang, Yuya Fujita, Xuankai Chang +1
Non-autoregressive (NAR) modeling has gained more and more attention in speech processing. With recent state-of-the-art attention-based automatic speech recognition (ASR) structure…
Speech Representation Learning Combining Conformer CPC with Deep Cluster for the ZeroSpeech Challenge 2021
Takashi Maekaku, Xuankai Chang, Yuya Fujita +3
We present a system for the Zero Resource Speech Challenge 2021, which combines a Contrastive Predictive Coding (CPC) with deep cluster. In deep cluster, we first prepare pseudo-la…
Toward Streaming ASR with Non-Autoregressive Insertion-based Model
Yuya Fujita, Tianzi Wang, Shinji Watanabe +1
Neural end-to-end (E2E) models have become a promising technique to realize practical automatic speech recognition (ASR) systems. When realizing such a system, one important issue…
Insertion-Based Modeling for End-to-End Automatic Speech Recognition
Yuya Fujita, Shinji Watanabe, Motoi Omachi +1
End-to-end (E2E) models have gained attention in the research field of automatic speech recognition (ASR). Many E2E models proposed so far assume left-to-right autoregressive gener…
Attention-based ASR with Lightweight and Dynamic Convolutions
Yuya Fujita, Aswin Shanmugam Subramanian, Motoi Omachi +1
End-to-end (E2E) automatic speech recognition (ASR) with sequence-to-sequence models has gained attention because of its simple model training compared with conventional hidden Mar…
An Investigation of End-to-End Multichannel Speech Recognition for Reverberant and Mismatch Conditions
Aswin Shanmugam Subramanian, Xiaofei Wang, Shinji Watanabe +3
Sequence-to-sequence (S2S) modeling is becoming a popular paradigm for automatic speech recognition (ASR) because of its ability to jointly optimize all the conventional ASR compon…
Speaker Selective Beamformer with Keyword Mask Estimation
Yusuke Kida, Dung Tran, Motoi Omachi +2
This paper addresses the problem of automatic speech recognition (ASR) of a target speaker in background speech. The novelty of our approach is that we focus on a wakeup keyword, w…