GAN-based statistical speech synthesis (in Japanese)Yuki Saito
Guest presentation at "Applied Gaussian Process and Machine Learning," Graduate School of Information Science and Technology, The University of Tokyo, Japan, 2021.
GAN-based statistical speech synthesis (in Japanese)Yuki Saito
Guest presentation at "Applied Gaussian Process and Machine Learning," Graduate School of Information Science and Technology, The University of Tokyo, Japan, 2021.
北村大地, 小野順貴, "独立性基準を用いた非負値行列因子分解の効果的な初期値決定法," 日本音響学会 2016年春季研究発表会, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
Tutorial on neural vocoders at the 2021 Speech Processing Courses in Crete, "Inclusive Neural Speech Synthesis."
Presenters: Xin Wang and Junichi Yamagishi, National Institute of Informatics, Japan
ゼロから始める深層強化学習（NLP2018講演資料）/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
北村大地, 小野順貴, "独立性基準を用いた非負値行列因子分解の効果的な初期値決定法," 日本音響学会 2016年春季研究発表会, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
Tutorial on neural vocoders at the 2021 Speech Processing Courses in Crete, "Inclusive Neural Speech Synthesis."
Presenters: Xin Wang and Junichi Yamagishi, National Institute of Informatics, Japan
ゼロから始める深層強化学習（NLP2018講演資料）/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
The document describes the NAIST Text-to-Speech system developed for the Blizzard Challenge 2015. The system uses an HMM-based approach with 4 main modules: text processing, speech processing, training, and synthesis. New functions include parameter trajectory smoothing using modulation spectrum analysis in the speech processing module and incorporating modulation spectrum in the synthesis module. Evaluation results show the system ranked highly in naturalness and intelligibility for the Marathi language.
Scan Segmentation Approach to Magnify Detection Sensitivity for Tiny Hardware...奈良先端大 情報科学研究科
This document presents a scan segmentation approach to magnify hardware Trojan detection sensitivity for tiny Trojans in integrated circuits. The proposed technique consists of four parts: 1) scan chain repartitioning to eliminate longest chains, 2) scan chain segmentation using clock gating, 3) applying test patterns using launch-on-capture mode to individual segments, and 4) generating Trojan detection golden patterns (TDGPs) as power fingerprints. Experimental results on benchmark circuit s1238 show the proposed method can detect a small combinational Trojan occupying 0.6% of the area, while normal methods without segmentation cannot. The technique aims to improve detection sensitivity for small Trojans.
![発表概要
問題： HMM音声合成 [Zen et al., 2009.] の音質劣化
– HMMの平滑化処理により発生
– 平滑化の影響を定量的に表現する特徴量とは？
平滑化を表現する特徴量とは？：
– パラメータ系列の分散 (GV: Global Variance) [Toda et al., 2007.]
– 系列の変調スペクトル (MS: Modulation Spectrum)を本発表で導入
提案法： 変調スペクトルを補償するポストフィルタ
– 生成パラメータ系列に対して、事前学習したフィルタを適用
HMM音声合成およびGVと比較して、合成音声の音質を改善
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