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Takaaki Hori

Researcher at Mitsubishi Electric Research Laboratories

Publications -  95
Citations -  7507

Takaaki Hori is an academic researcher from Mitsubishi Electric Research Laboratories. The author has contributed to research in topics: Language model & Speech processing. The author has an hindex of 36, co-authored 88 publications receiving 5271 citations. Previous affiliations of Takaaki Hori include Nippon Telegraph and Telephone & Mitsubishi Electric.

Papers
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Proceedings ArticleDOI

ESPNet: End-to-end speech processing toolkit

TL;DR: In this article, a new open source platform for end-to-end speech processing named ESPnet is introduced, which mainly focuses on automatic speech recognition (ASR), and adopts widely used dynamic neural network toolkits, Chainer and PyTorch, as a main deep learning engine.
Journal ArticleDOI

Hybrid CTC/Attention Architecture for End-to-End Speech Recognition

TL;DR: The proposed hybrid CTC/attention end-to-end ASR is applied to two large-scale ASR benchmarks, and exhibits performance that is comparable to conventional DNN/HMM ASR systems based on the advantages of both multiobjective learning and joint decoding without linguistic resources.
Proceedings ArticleDOI

Joint CTC-attention based end-to-end speech recognition using multi-task learning

TL;DR: This paper proposed a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder framework for end-to-end speech recognition, which can improve robustness and achieve fast convergence by using a joint CTC-attention model.
Posted Content

Joint CTC-Attention based End-to-End Speech Recognition using Multi-task Learning

TL;DR: A novel method for end-to-end speech recognition to improve robustness and achieve fast convergence by using a joint CTC-attention model within the multi-task learning framework, thereby mitigating the alignment issue.
Posted Content

ESPnet: End-to-End Speech Processing Toolkit

TL;DR: A major architecture of this software platform, several important functionalities, which differentiate ESPnet from other open source ASR toolkits, and experimental results with major ASR benchmarks are explained.