528 citations · 562 across the 12 of their papers we have counts for
7 papers · 1 filter
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition
Raden Mu'az Mun'im, Nakamasa Inoue, Koichi Shinoda
We investigate the feasibility of sequence-level knowledge distillation of Sequence-to-Sequence (Seq2Seq) models for Large Vocabulary Continuous Speech Recognition (LVSCR). We firs…
Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances
Thao Minh Le, Nobuyuki Shimizu, Takashi Miyazaki +1
With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect suc…
Few-Shot Adaptation for Multimedia Semantic Indexing
Nakamasa Inoue, Koichi Shinoda
We propose a few-shot adaptation framework, which bridges zero-shot learning and supervised many-shot learning, for semantic indexing of image and video data. Few-shot adaptation p…
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition
Thao Minh Le, Nakamasa Inoue, Koichi Shinoda
This paper presents a new framework for human action recognition from a 3D skeleton sequence. Previous studies do not fully utilize the temporal relationships between video segment…
I-vector Transformation Using Conditional Generative Adversarial Networks for Short Utterance Speaker Verification
Jiacen Zhang, Nakamasa Inoue, Koichi Shinoda
I-vector based text-independent speaker verification (SV) systems often have poor performance with short utterances, as the biased phonetic distribution in a short utterance makes…
Detecting Alzheimer's Disease Using Gated Convolutional Neural Network from Audio Data
Tifani Warnita, Nakamasa Inoue, Koichi Shinoda
We propose an automatic detection method of Alzheimer's diseases using a gated convolutional neural network (GCNN) from speech data. This GCNN can be trained with a relatively smal…
Attentive Statistics Pooling for Deep Speaker Embedding
Koji Okabe, Takafumi Koshinaka, Koichi Shinoda
This paper proposes attentive statistics pooling for deep speaker embedding in text-independent speaker verification. In conventional speaker embedding, frame-level features are av…