Search Results for author: Hiromitsu Nishizaki

Found 9 papers, 1 papers with code

Handwritten Character Generation using Y-Autoencoder for Character Recognition Model Training

no code implementations LREC 2022 Tomoki Kitagawa, Chee Siang Leow, Hiromitsu Nishizaki

This paper introduces a Y-Autoencoder (Y-AE)-based handwritten character generator to generate multiple Japanese Hiragana characters with a single image to increase the amount of data for training a handwritten character recognizer.

Optical Character Recognition Optical Character Recognition (OCR)

Frequency-Directional Attention Model for Multilingual Automatic Speech Recognition

no code implementations29 Mar 2022 Akihiro Dobashi, Chee Siang Leow, Hiromitsu Nishizaki

Furthermore, visualization of the attention weights based on the proposed method suggested that it is possible to transform acoustic features considering the frequency characteristics of each language.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Peer Collaborative Learning for Polyphonic Sound Event Detection

no code implementations7 Oct 2021 Hayato Endo, Hiromitsu Nishizaki

This paper describes that semi-supervised learning called peer collaborative learning (PCL) can be applied to the polyphonic sound event detection (PSED) task, which is one of the tasks in the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge.

Event Detection Knowledge Distillation +1

ExKaldi-RT: A Real-Time Automatic Speech Recognition Extension Toolkit of Kaldi

1 code implementation3 Apr 2021 Yu Wang, Chee Siang Leow, Akio Kobayashi, Takehito Utsuro, Hiromitsu Nishizaki

This paper describes the ExKaldi-RT online automatic speech recognition (ASR) toolkit that is implemented based on the Kaldi ASR toolkit and Python language.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Integrating Disfluency-based and Prosodic Features with Acoustics in Automatic Fluency Evaluation of Spontaneous Speech

no code implementations LREC 2020 Huaijin Deng, Youchao Lin, Takehito Utsuro, Akio Kobayashi, Hiromitsu Nishizaki, Junichi Hoshino

The experimental evaluation results of those integrated diverse features indicate that time sequential acoustic features contribute to improving the model with disfluency-based and prosodic features when detecting fluent speech, but not when detecting disfluent speech.

Improving Speech Recognition for the Elderly: A New Corpus of Elderly Japanese Speech and Investigation of Acoustic Modeling for Speech Recognition

no code implementations LREC 2020 Meiko Fukuda, Hiromitsu Nishizaki, Yurie Iribe, Ryota Nishimura, Norihide Kitaoka

In an aging society like Japan, a highly accurate speech recognition system is needed for use in electronic devices for the elderly, but this level of accuracy cannot be obtained using conventional speech recognition systems due to the unique features of the speech of elderly people.

speech-recognition Speech Recognition

Audio Classification of Bit-Representation Waveform

no code implementations8 Apr 2019 Masaki Okawa, Takuya Saito, Naoki Sawada, Hiromitsu Nishizaki

In our experiment, we compare the proposed bit representation waveform, which is directly given to a neural network, to other representations of audio waveforms such as a raw audio waveform and a power spectrum with two classification tasks: one is an acoustic event classification task and the other is a sound/music classification task.

Audio Classification Event Detection +4

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