Search Results for author: Tomoki Hayashi

Found 32 papers, 15 papers with code

ESPnet-ONNX: Bridging a Gap Between Research and Production

1 code implementation20 Sep 2022 Masao Someki, Yosuke Higuchi, Tomoki Hayashi, Shinji Watanabe

In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks.

Spoken Language Understanding

A Comparative Study of Self-supervised Speech Representation Based Voice Conversion

no code implementations10 Jul 2022 Wen-Chin Huang, Shu-wen Yang, Tomoki Hayashi, Tomoki Toda

We present a large-scale comparative study of self-supervised speech representation (S3R)-based voice conversion (VC).

Voice Conversion

Acoustic Event Detection with Classifier Chains

no code implementations17 Feb 2022 Tatsuya Komatsu, Shinji Watanabe, Koichi Miyazaki, Tomoki Hayashi

In each iteration, the event's activity is estimated and used to condition the next output based on the probabilistic chain rule to form classifier chains.

Event Detection

Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem

no code implementations17 Dec 2021 Jing Shi, Xuankai Chang, Tomoki Hayashi, Yen-Ju Lu, Shinji Watanabe, Bo Xu

Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols, and convert the paradigm of the speech separation/enhancement related tasks from regression to classification.

Speech Separation

ViCE: Visual Concept Embedding Discovery and Superpixelization

no code implementations24 Nov 2021 Robin Karlsson, Tomoki Hayashi, Keisuke Fujii, Alexander Carballo, Kento Ohtani, Kazuya Takeda

Recent self-supervised computer vision methods have demonstrated equal or better performance to supervised methods, opening for AI systems to learn visual representations from practically unlimited data.

Domain Generalization Learning Word Embeddings +2

S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations

1 code implementation12 Oct 2021 Wen-Chin Huang, Shu-wen Yang, Tomoki Hayashi, Hung-Yi Lee, Shinji Watanabe, Tomoki Toda

In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting.

Voice Conversion

On Prosody Modeling for ASR+TTS based Voice Conversion

no code implementations20 Jul 2021 Wen-Chin Huang, Tomoki Hayashi, Xinjian Li, Shinji Watanabe, Tomoki Toda

In voice conversion (VC), an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents; these are then used as input by a text-to-speech (TTS) system to generate the converted speech.

Automatic Speech Recognition speech-recognition +1

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

Non-autoregressive sequence-to-sequence voice conversion

no code implementations14 Apr 2021 Tomoki Hayashi, Wen-Chin Huang, Kazuhiro Kobayashi, Tomoki Toda

This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models.

Voice Conversion

Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations

no code implementations23 Oct 2020 Wen-Chin Huang, Yi-Chiao Wu, Tomoki Hayashi, Tomoki Toda

Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter.

Voice Conversion

Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network

1 code implementation25 Jul 2020 Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto, Hisashi Kawai, Tomoki Toda

To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary $F_{0}$ feature.

Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network

1 code implementation11 Jul 2020 Yi-Chiao Wu, Tomoki Hayashi, Patrick Lumban Tobing, Kazuhiro Kobayashi, Tomoki Toda

In this paper, a pitch-adaptive waveform generative model named Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch controllability of vanilla WaveNet (WN) using pitch-dependent dilated convolution neural networks (PDCNNs).

Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation

1 code implementation18 May 2020 Yi-Chiao Wu, Tomoki Hayashi, Takuma Okamoto, Hisashi Kawai, Tomoki Toda

In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG.

Audio and Speech Processing Sound

DiscreTalk: Text-to-Speech as a Machine Translation Problem

no code implementations12 May 2020 Tomoki Hayashi, Shinji Watanabe

This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT).

Automatic Speech Recognition Machine Translation +2

Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining

no code implementations14 Dec 2019 Wen-Chin Huang, Tomoki Hayashi, Yi-Chiao Wu, Hirokazu Kameoka, Tomoki Toda

We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining.

Voice Conversion

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

Non-Parallel Voice Conversion with Cyclic Variational Autoencoder

2 code implementations24 Jul 2019 Patrick Lumban Tobing, Yi-Chiao Wu, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda

In this work, to overcome this problem, we propose to use CycleVAE-based spectral model that indirectly optimizes the conversion flow by recycling the converted features back into the system to obtain corresponding cyclic reconstructed spectra that can be directly optimized.

Voice Conversion

Statistical Voice Conversion with Quasi-Periodic WaveNet Vocoder

1 code implementation21 Jul 2019 Yi-Chiao Wu, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda

However, because of the fixed dilated convolution and generic network architecture, the WN vocoder lacks robustness against unseen input features and often requires a huge network size to achieve acceptable speech quality.

Audio and Speech Processing Sound

Quasi-Periodic WaveNet Vocoder: A Pitch Dependent Dilated Convolution Model for Parametric Speech Generation

1 code implementation1 Jul 2019 Yi-Chiao Wu, Tomoki Hayashi, Patrick Lumban Tobing, Kazuhiro Kobayashi, Tomoki Toda

In this paper, we propose a quasi-periodic neural network (QPNet) vocoder with a novel network architecture named pitch-dependent dilated convolution (PDCNN) to improve the pitch controllability of WaveNet (WN) vocoder.

Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion

no code implementations27 Nov 2018 Wen-Chin Huang, Yi-Chiao Wu, Hsin-Te Hwang, Patrick Lumban Tobing, Tomoki Hayashi, Kazuhiro Kobayashi, Tomoki Toda, Yu Tsao, Hsin-Min Wang

Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation.

Voice Conversion

Cycle-consistency training for end-to-end speech recognition

no code implementations2 Nov 2018 Takaaki Hori, Ramon Astudillo, Tomoki Hayashi, Yu Zhang, Shinji Watanabe, Jonathan Le Roux

To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal.

Automatic Speech Recognition speech-recognition

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 +3

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.

speech-recognition Speech Recognition

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