学生のHO, Tuan VuさんがNCSP2018においてStudent Best Paper Awardsを受賞
Student Best Paper AwardsはNCSP2018における学生発表者(兼 第一著者)による発表論文のうち、ワークショップ委員会において特に優れた論文と認められたものに対して授与される論文賞です。
「Non-parallel Dictionary-based Voice Conversion using Variational Autoencoder with Modulation Spectrum-constrained Training」
In this paper, we present a non-parallel voice conversion (VC) approach that does not require parallel data or linguistic labeling for training process. Dictionary-based voice conversion is the class of methods aiming to decomposed speech into separate factors for manipulation. Non-negative matrix factorization (NMF) is the most common method to decompose input spectrum into a weighted linear combination of a set of dictionaries (basis) and weights. However, the requirement for parallel training data in this method causes several problems: 1) limited practical usability when parallel data are not available, 2) additional error from alignment process degrades output speech quality. In order to alleviate these problems, this paper presents a dictionary-based VC approach by incorporating a Variational Autoencoder (VAE) to decomposed input speech spectrum into speaker dictionary and weights without parallel training data. According to evaluation results, the proposed method achieved better speech naturalness while retaining the same speaker similarity as NMF-based VC even though un-aligned data is used.
I would like to express my appreciation to the NCSP'18 committee board and Research Institute of Signal Processing Japan for recognizing me with the "NCSP'18 Student Paper Award". I am truly honor to receive it. I would like to express my sincere gratitude to Professor Masato Akagi, my supervisor at Japan Advanced Institute of Science and Technology, as all this work cannot be possible without the kind support from him. I also want to extend my appreciation to all the lab members at Acoustic Information Science Laboratory for all the help and support they provided me during this study. This award is an important milestone that will encourage me to keep on working harder in the future.