33 citations · 49 across the 11 of their papers we have counts for
11 papers
Harnessing the Zero-Shot Power of Instruction-Tuned Large Language Model in End-to-End Speech Recognition
Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi
We propose to utilize an instruction-tuned large language model (LLM) for guiding the text generation process in automatic speech recognition (ASR). Modern large language models (L…
Conversation-oriented ASR with multi-look-ahead CBS architecture
Huaibo Zhao, Shinya Fujie, Tetsuji Ogawa +3
During conversations, humans are capable of inferring the intention of the speaker at any point of the speech to prepare the following action promptly. Such ability is also the key…
InterMPL: Momentum Pseudo-Labeling with Intermediate CTC Loss
Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi +1
This paper presents InterMPL, a semi-supervised learning method of end-to-end automatic speech recognition (ASR) that performs pseudo-labeling (PL) with intermediate supervision. M…
BECTRA: Transducer-based End-to-End ASR with BERT-Enhanced Encoder
Yosuke Higuchi, Tetsuji Ogawa, Tetsunori Kobayashi +1
We present BERT-CTC-Transducer (BECTRA), a novel end-to-end automatic speech recognition (E2E-ASR) model formulated by the transducer with a BERT-enhanced encoder. Integrating a la…
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
Yosuke Higuchi, Brian Yan, Siddhant Arora +3
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC). Our formulation relaxes the con…
An Investigation of Enhancing CTC Model for Triggered Attention-based Streaming ASR
Huaibo Zhao, Yosuke Higuchi, Tetsuji Ogawa +1
In the present paper, an attempt is made to combine Mask-CTC and the triggered attention mechanism to construct a streaming end-to-end automatic speech recognition (ASR) system tha…
Hierarchical Conditional End-to-End ASR with CTC and Multi-Granular Subword Units
Yosuke Higuchi, Keita Karube, Tetsuji Ogawa +1
In end-to-end automatic speech recognition (ASR), a model is expected to implicitly learn representations suitable for recognizing a word-level sequence. However, the huge abstract…
Exploring Effectiveness of Inter-Microtask Qualification Tests in Crowdsourcing
Masaya Morinaga, Susumu Saito, Teppei Nakano +2
Qualification tests in crowdsourcing are often used to pre-filter workers by measuring their ability in executing microtasks.While creating qualification tests for each task type i…
Improved Mask-CTC for Non-Autoregressive End-to-End ASR
Yosuke Higuchi, Hirofumi Inaguma, Shinji Watanabe +2
For real-world deployment of automatic speech recognition (ASR), the system is desired to be capable of fast inference while relieving the requirement of computational resources. T…
Mask CTC: Non-Autoregressive End-to-End ASR with CTC and Mask Predict
Yosuke Higuchi, Shinji Watanabe, Nanxin Chen +2
We present Mask CTC, a novel non-autoregressive end-to-end automatic speech recognition (ASR) framework, which generates a sequence by refining outputs of the connectionist tempora…
TurkScanner: Predicting the Hourly Wage of Microtasks
Susumu Saito, Chun-Wei Chiang, Saiph Savage +3
Workers in crowd markets struggle to earn a living. One reason for this is that it is difficult for workers to accurately gauge the hourly wages of microtasks, and they consequentl…