48 citations · 214 across the 19 of their papers we have counts for
6 papers · 1 filter
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss
Hiroshi Sato, Ryo Masumura, Tsubasa Ochiai +8
Self-supervised learning (SSL) is the latest breakthrough in speech processing, especially for label-scarce downstream tasks by leveraging massive unlabeled audio data. The noise r…
Model architectures to extrapolate emotional expressions in DNN-based text-to-speech
Katsuki Inoue, Sunao Hara, Masanobu Abe +2
This paper proposes architectures that facilitate the extrapolation of emotional expressions in deep neural network (DNN)-based text-to-speech (TTS). In this study, the meaning of…
Nonparallel Voice Conversion with Augmented Classifier Star Generative Adversarial Networks
Hirokazu Kameoka, Takuhiro Kaneko, Kou Tanaka +1
We previously proposed a method that allows for nonparallel voice conversion (VC) by using a variant of generative adversarial networks (GANs) called StarGAN. The main features of…
Many-to-Many Voice Transformer Network
Hirokazu Kameoka, Wen-Chin Huang, Kou Tanaka +3
This paper proposes a voice conversion (VC) method based on a sequence-to-sequence (S2S) learning framework, which enables simultaneous conversion of the voice characteristics, pit…
AttS2S-VC: Sequence-to-Sequence Voice Conversion with Attention and Context Preservation Mechanisms
Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko +1
This paper describes a method based on a sequence-to-sequence learning (Seq2Seq) with attention and context preservation mechanism for voice conversion (VC) tasks. Seq2Seq has been…
WaveCycleGAN: Synthetic-to-natural speech waveform conversion using cycle-consistent adversarial networks
Kou Tanaka, Takuhiro Kaneko, Nobukatsu Hojo +1
We propose a learning-based filter that allows us to directly modify a synthetic speech waveform into a natural speech waveform. Speech-processing systems using a vocoder framework…