no code implementations • 20 Nov 2023 • Hanako Segawa, Tsubasa Ochiai, Marc Delcroix, Tomohiro Nakatani, Rintaro Ikeshita, Shoko Araki, Takeshi Yamada, Shoji Makino
However, this training objective may not be optimal for a specific array processing back-end, such as beamforming.
no code implementations • IEEE Access 2022 • JENNIFER SANTOSO, Takeshi Yamada, Kenkichi Ishizuka, Taiichi Hashimoto, Shoji Makino
Although there is a method to improve ASR performance in the presence of emotional speech, it requires the fine-tuning of ASR, which has a high computational cost and leads to the loss of cues important for determining the presence of emotion in speech segments, which can be helpful in SER.
Ranked #4 on Multimodal Emotion Recognition on IEMOCAP
Multimodal Emotion Recognition Speech Emotion Recognition +2
1 code implementation • NeurIPS 2021 • Masahiro Nakano, Yasuhiro Fujiwara, Akisato Kimura, Takeshi Yamada, Naonori Ueda
Our main contribution is to introduce the notion of permutons into the well-known Chinese restaurant process (CRP) for sequence partitioning: a permuton is a probability measure on $[0, 1]\times [0, 1]$ and can be regarded as a geometric interpretation of the scaling limit of permutations.
1 code implementation • NeurIPS 2020 • Masahiro Nakano, Akisato Kimura, Takeshi Yamada, Naonori Ueda
Compared with conventional BNP models for arbitrary RPs, the proposed model is simpler and has a high affinity with Bayesian inference.
no code implementations • NeurIPS 2015 • Yuya Yoshikawa, Tomoharu Iwata, Hiroshi Sawada, Takeshi Yamada
We propose a kernel-based method for finding matching between instances across different domains, such as multilingual documents and images with annotations.
no code implementations • NeurIPS 2009 • Tomoharu Iwata, Takeshi Yamada, Naonori Ueda
We propose a probabilistic topic model for analyzing and extracting content-related annotations from noisy annotated discrete data such as web pages stored in social bookmarking services.