Search Results for author: Takafumi Moriya

Found 7 papers, 1 papers with code

On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for Multimodal Sentiment Analysis

1 code implementation28 Oct 2022 Atsushi Ando, Ryo Masumura, Akihiko Takashima, Satoshi Suzuki, Naoki Makishima, Keita Suzuki, Takafumi Moriya, Takanori Ashihara, Hiroshi Sato

This paper investigates the effectiveness and implementation of modality-specific large-scale pre-trained encoders for multimodal sentiment analysis~(MSA).

Multimodal Sentiment Analysis

Streaming Target-Speaker ASR with Neural Transducer

no code implementations9 Sep 2022 Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Takahiro Shinozaki

We confirm in experiments that our TS-ASR achieves comparable recognition performance with conventional cascade systems in the offline setting, while reducing computation costs and realizing streaming TS-ASR.

Automatic Speech Recognition Speech Extraction +1

Deep versus Wide: An Analysis of Student Architectures for Task-Agnostic Knowledge Distillation of Self-Supervised Speech Models

no code implementations14 Jul 2022 Takanori Ashihara, Takafumi Moriya, Kohei Matsuura, Tomohiro Tanaka

We investigate the performance on SUPERB while varying the structure and KD methods so as to keep the number of parameters constant; this allows us to analyze the contribution of the representation introduced by varying the model architecture.

Automatic Speech Recognition Knowledge Distillation +3

Strategies to Improve Robustness of Target Speech Extraction to Enrollment Variations

no code implementations16 Jun 2022 Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Takafumi Moriya, Naoki Makishima, Mana Ihori, Tomohiro Tanaka, Ryo Masumura

Experimental validation reveals the effectiveness of both worst-enrollment target training and SI-loss training to improve robustness against enrollment variations, by increasing speaker discriminability.

Speaker Identification Speech Extraction

Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition

no code implementations4 Jul 2021 Tomohiro Tanaka, Ryo Masumura, Mana Ihori, Akihiko Takashima, Takafumi Moriya, Takanori Ashihara, Shota Orihashi, Naoki Makishima

However, the conventional method cannot take into account the relationships between these two different modal inputs because the input contexts are separately encoded for each modal.

Automatic Speech Recognition speech-recognition

Should We Always Separate?: Switching Between Enhanced and Observed Signals for Overlapping Speech Recognition

no code implementations2 Jun 2021 Hiroshi Sato, Tsubasa Ochiai, Marc Delcroix, Keisuke Kinoshita, Takafumi Moriya, Naoyuki Kamo

', we analyze ASR performance on observed and enhanced speech at various noise and interference conditions, and show that speech enhancement degrades ASR under some conditions even for overlapping speech.

Automatic Speech Recognition Speech Enhancement +2

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