Search Results for author: Kazuya Takeda

Found 14 papers, 5 papers with code

Learning a Model for Inferring a Spatial Road Lane Network Graph using Self-Supervision

1 code implementation5 Jul 2021 Robin Karlsson, David Robert Wong, Simon Thompson, Kazuya Takeda

A formal road lane network model is presented and proves that any structured road scene can be represented by a directed acyclic graph of at most depth three while retaining the notion of intersection regions, and that this is the most compressed representation.

Autonomous Vehicles Self-Supervised Learning

Anomalous Sound Detection Using a Binary Classification Model and Class Centroids

no code implementations11 Jun 2021 Ibuki Kuroyanagi, Tomoki Hayashi, Kazuya Takeda, Tomoki Toda

Our results showed that multi-task learning using binary classification and metric learning to consider the distance from each class centroid in the feature space is effective, and performance can be significantly improved by using even a small amount of anomalous data during training.

Classification Metric Learning +1

Policy learning with partial observation and mechanical constraints for multi-person modeling

1 code implementation7 Jul 2020 Keisuke Fujii, Naoya Takeishi, Yoshinobu Kawahara, Kazuya Takeda

Extracting the rules of real-world biological multi-agent behaviors is a current challenge in various scientific and engineering fields.

Imitation Learning

Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform

no code implementations3 Apr 2020 Alexander Carballo, Abraham Monrroy, David Wong, Patiphon Narksri, Jacob Lambert, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open source platform Autoware.

LIBRE: The Multiple 3D LiDAR Dataset

no code implementations13 Mar 2020 Alexander Carballo, Jacob Lambert, Abraham Monrroy-Cano, David Robert Wong, Patiphon Narksri, Yuki Kitsukawa, Eijiro Takeuchi, Shinpei Kato, Kazuya Takeda

In this work, we present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors, covering a range of manufacturers, models, and laser configurations.

ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit

3 code implementations24 Oct 2019 Tomoki Hayashi, Ryuichi Yamamoto, Katsuki Inoue, Takenori Yoshimura, Shinji Watanabe, Tomoki Toda, Kazuya Takeda, Yu Zhang, Xu Tan

Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.

automatic-speech-recognition Speech Recognition

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

no code implementations12 Jun 2019 Ekim Yurtsever, Jacob Lambert, Alexander Carballo, Kazuya Takeda

Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience.

Robotics Systems and Control Systems and Control

Back-Translation-Style Data Augmentation for End-to-End ASR

no code implementations28 Jul 2018 Tomoki Hayashi, Shinji Watanabe, Yu Zhang, Tomoki Toda, Takaaki Hori, Ramon Astudillo, Kazuya Takeda

In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.

automatic-speech-recognition Data Augmentation +4

Multi-Head Decoder for End-to-End Speech Recognition

no code implementations22 Apr 2018 Tomoki Hayashi, Shinji Watanabe, Tomoki Toda, Kazuya Takeda

This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model.

End-To-End Speech Recognition Speech Recognition

Causal analysis of task completion errors in spoken music retrieval interactions

no code implementations LREC 2012 Sunao Hara, Norihide Kitaoka, Kazuya Takeda

In this paper, we analyze the causes of task completion errors in spoken dialog systems, using a decision tree with N-gram features of the dialog to detect task-incomplete dialogs.

General Classification

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