4 papers
Wave-Trainer-Fit: Neural Vocoder with Trainable Prior and Fixed-Point Iteration towards High-Quality Speech Generation from SSL features
Hien Ohnaka, Yuma Shirahata, Masaya Kawamura
We propose WaveTrainerFit, a neural vocoder that performs high-quality waveform generation from data-driven features such as SSL features. WaveTrainerFit builds upon the WaveFit vo…
Grapheme-Coherent Phonemic and Prosodic Annotation of Speech by Implicit and Explicit Grapheme Conditioning
Hien Ohnaka, Yuma Shirahata, Byeongseon Park +1
We propose a model to obtain phonemic and prosodic labels of speech that are coherent with graphemes. Unlike previous methods that simply fine-tune a pre-trained ASR model with the…
Unsupervised speech enhancement with spectral kurtosis and double deep priors
Hien Ohnaka, Ryoichi Miyazaki
This paper proposes an unsupervised DNN-based speech enhancement approach founded on deep priors (DPs). Here, DP signifies that DNNs are more inclined to produce clean speech signa…
Visual onoma-to-wave: environmental sound synthesis from visual onomatopoeias and sound-source images
Hien Ohnaka, Shinnosuke Takamichi, Keisuke Imoto +3
We propose a method for synthesizing environmental sounds from visually represented onomatopoeias and sound sources. An onomatopoeia is a word that imitates a sound structure, i.e.…