6 papers · 1 filter
Exploring Pre-training Benefits on Phoneme Addition through Fine-tuning in Speech Synthesis
Masato Murata, Koichi Miyazaki, Tomoki Koriyama +1
Transfer learning is widely used for low-resource text-to-speech. When the target corpus contains phonemes unseen in pre-training, the model must expand its phoneme inventory durin…
Speaker-agnostic Emotion Vector for Cross-speaker Emotion Intensity Control
Masato Murata, Koichi Miyazaki, Tomoki Koriyama
Cross-speaker emotion intensity control aims to generate emotional speech of a target speaker with desired emotion intensities using only their neutral speech. A recently proposed…
Eigenvoice Synthesis based on Model Editing for Speaker Generation
Masato Murata, Koichi Miyazaki, Tomoki Koriyama +1
Speaker generation task aims to create unseen speaker voice without reference speech. The key to the task is defining a speaker space that represents diverse speakers to determine…
Mamba-based Decoder-Only Approach with Bidirectional Speech Modeling for Speech Recognition
Yoshiki Masuyama, Koichi Miyazaki, Masato Murata
Selective state space models (SSMs) represented by Mamba have demonstrated their computational efficiency and promising outcomes in various tasks, including automatic speech recogn…
An Attribute Interpolation Method in Speech Synthesis by Model Merging
Masato Murata, Koichi Miyazaki, Tomoki Koriyama
With the development of speech synthesis, recent research has focused on challenging tasks, such as speaker generation and emotion intensity control. Attribute interpolation is a c…
Exploring the Capability of Mamba in Speech Applications
Koichi Miyazaki, Yoshiki Masuyama, Masato Murata
This paper explores the capability of Mamba, a recently proposed architecture based on state space models (SSMs), as a competitive alternative to Transformer-based models. In the s…