Search Results for author: Ryo Masumura

Found 23 papers, 0 papers with code

Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages

no code implementations24 Nov 2021 Shota Orihashi, Yoshihiro Yamazaki, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Ryo Masumura

To this end, the proposed method pre-trains the encoder by using a multilingual dataset that combines the resource-poor language's dataset and the resource-rich language's dataset to learn language-invariant knowledge for scene text recognition.

Scene Text Recognition

Hierarchical Knowledge Distillation for Dialogue Sequence Labeling

no code implementations22 Nov 2021 Shota Orihashi, Yoshihiro Yamazaki, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Ryo Masumura

Dialogue sequence labeling is a supervised learning task that estimates labels for each utterance in the target dialogue document, and is useful for many applications such as dialogue act estimation.

Knowledge Distillation Scene Segmentation

End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning

no code implementations7 Jul 2021 Tomohiro Tanaka, Ryo Masumura, Mana Ihori, Akihiko Takashima, Shota Orihashi, Naoki Makishima

We propose a semi-supervised learning method for building end-to-end rich transcription-style automatic speech recognition (RT-ASR) systems from small-scale rich transcription-style and large-scale common transcription-style datasets.

Speech Recognition

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.

Speech Recognition

Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens

no code implementations23 Jun 2021 Mana Ihori, Naoki Makishima, Tomohiro Tanaka, Akihiko Takashima, Shota Orihashi, Ryo Masumura

To execute multiple conversion tasks simultaneously without preparing matched datasets, our key idea is to distinguish individual conversion tasks using the on-off switch.

Speech Recognition Zero-Shot Learning

Audio-Visual Speech Separation Using Cross-Modal Correspondence Loss

no code implementations2 Mar 2021 Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Shota Orihashi, Ryo Masumura

We present an audio-visual speech separation learning method that considers the correspondence between the separated signals and the visual signals to reflect the speech characteristics during training.

Speech Separation

Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents

no code implementations16 Feb 2021 Ryo Masumura, Naoki Makishima, Mana Ihori, Akihiko Takashima, Tomohiro Tanaka, Shota Orihashi

This paper presents a novel self-supervised learning method for handling conversational documents consisting of transcribed text of human-to-human conversations.

Language Modelling Representation Learning +2

MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training

no code implementations15 Feb 2021 Mana Ihori, Naoki Makishima, Tomohiro Tanaka, Akihiko Takashima, Shota Orihashi, Ryo Masumura

However, these models require a large amount of paired data of spoken-style text and style normalized text, and it is difficult to prepare such a volume of data.

Machine Translation Self-Supervised Learning

Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model

no code implementations INLG (ACL) 2020 Mana Ihori, Ryo Masumura, Naoki Makishima, Tomohiro Tanaka, Akihiko Takashima, Shota Orihashi

Thus, it is important to leverage memorized knowledge in the external LM for building the seq2seq model, since it is hard to prepare a large amount of paired data.

Language Modelling

A Transformer-based Audio Captioning Model with Keyword Estimation

no code implementations1 Jul 2020 Yuma Koizumi, Ryo Masumura, Kyosuke Nishida, Masahiro Yasuda, Shoichiro Saito

TRACKE estimates keywords, which comprise a word set corresponding to audio events/scenes in the input audio, and generates the caption while referring to the estimated keywords to reduce word-selection indeterminacy.

Acoustic Scene Classification Audio captioning +2

Parallel Corpus for Japanese Spoken-to-Written Style Conversion

no code implementations LREC 2020 Mana Ihori, Akihiko Takashima, Ryo Masumura

Therefore, we created a new Japanese parallel corpus of spoken-style text and written-style text that can simultaneously handle general problems and Japanese-specific ones.

Speech Recognition

Generating Responses that Reflect Meta Information in User-Generated Question Answer Pairs

no code implementations LREC 2020 Takashi Kodama, Ryuichiro Higashinaka, Koh Mitsuda, Ryo Masumura, Yushi Aono, Ryuta Nakamura, Noritake Adachi, Hidetoshi Kawabata

This paper concerns the problem of realizing consistent personalities in neural conversational modeling by using user generated question-answer pairs as training data.

Question Answering

Multi-task and Multi-lingual Joint Learning of Neural Lexical Utterance Classification based on Partially-shared Modeling

no code implementations COLING 2018 Ryo Masumura, Tomohiro Tanaka, Ryuichiro Higashinaka, Hirokazu Masataki, Yushi Aono

In addition, in order to effectively transfer knowledge between different task data sets and different language data sets, this paper proposes a partially-shared modeling method that possesses both shared components and components specific to individual data sets.

Feature Engineering General Classification +2

Neural Dialogue Context Online End-of-Turn Detection

no code implementations WS 2018 Ryo Masumura, Tomohiro Tanaka, Atsushi Ando, Ryo Ishii, Ryuichiro Higashinaka, Yushi Aono

This paper proposes a fully neural network based dialogue-context online end-of-turn detection method that can utilize long-range interactive information extracted from both speaker{'}s utterances and collocutor{'}s utterances.

Action Detection Spoken Dialogue Systems

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