1 citations · 2 across the 6 of their papers we have counts for
6 papers
Factor-Conditioned Speaking-Style Captioning
Atsushi Ando, Takafumi Moriya, Shota Horiguchi +1
This paper presents a novel speaking-style captioning method that generates diverse descriptions while accurately predicting speaking-style information. Conventional learning crite…
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff
Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda +4
This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs). Although adversarial train…
End-to-End Joint Target and Non-Target Speakers ASR
Ryo Masumura, Naoki Makishima, Taiga Yamane +12
This paper proposes a novel automatic speech recognition (ASR) system that can transcribe individual speaker's speech while identifying whether they are target or non-target speake…
On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for Multimodal Sentiment Analysis
Atsushi Ando, Ryo Masumura, Akihiko Takashima +6
This paper investigates the effectiveness and implementation of modality-specific large-scale pre-trained encoders for multimodal sentiment analysis~(MSA). Although the effectivene…
Speaker consistency loss and step-wise optimization for semi-supervised joint training of TTS and ASR using unpaired text data
Naoki Makishima, Satoshi Suzuki, Atsushi Ando +1
In this paper, we investigate the semi-supervised joint training of text to speech (TTS) and automatic speech recognition (ASR), where a small amount of paired data and a large amo…
Does the Lombard Effect Improve Emotional Communication in Noise? - Analysis of Emotional Speech Acted in Noise -
Yi Zhao, Atsushi Ando, Shinji Takaki +2
Speakers usually adjust their way of talking in noisy environments involuntarily for effective communication. This adaptation is known as the Lombard effect. Although speech accomp…