28 citations · 44 across the 25 of their papers we have counts for
6 papers · 1 filter
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…
Streaming Target-Speaker ASR with Neural Transducer
Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai +2
Although recent advances in deep learning technology have boosted automatic speech recognition (ASR) performance in the single-talker case, it remains difficult to recognize multi-…
Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +6
Target speech extraction is a technique to extract the target speaker's voice from mixture signals using a pre-recorded enrollment utterance that characterize the voice characteris…
Listen only to me! How well can target speech extraction handle false alarms?
Marc Delcroix, Keisuke Kinoshita, Tsubasa Ochiai +3
Target speech extraction (TSE) extracts the speech of a target speaker in a mixture given auxiliary clues characterizing the speaker, such as an enrollment utterance. TSE addresses…
Learning to Enhance or Not: Neural Network-Based Switching of Enhanced and Observed Signals for Overlapping Speech Recognition
Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix +3
The combination of a deep neural network (DNN) -based speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end is a widely used approach to implement ov…
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4
It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with single-channel speech enhancement (SE). In this paper, we investigate the cause…