2 citations · 5 across the 21 of their papers we have counts for
12 papers · 1 filter
Utilizing Resource-Rich Language Datasets for End-to-End Scene Text Recognition in Resource-Poor Languages
Shota Orihashi, Yoshihiro Yamazaki, Naoki Makishima +4
This paper presents a novel training method for end-to-end scene text recognition. End-to-end scene text recognition offers high recognition accuracy, especially when using the enc…
Hierarchical Knowledge Distillation for Dialogue Sequence Labeling
Shota Orihashi, Yoshihiro Yamazaki, Naoki Makishima +4
This paper presents a novel knowledge distillation method for dialogue sequence labeling. Dialogue sequence labeling is a supervised learning task that estimates labels for each ut…
End-to-End Rich Transcription-Style Automatic Speech Recognition with Semi-Supervised Learning
Tomohiro Tanaka, Ryo Masumura, Mana Ihori +3
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-styl…
Cross-Modal Transformer-Based Neural Correction Models for Automatic Speech Recognition
Tomohiro Tanaka, Ryo Masumura, Mana Ihori +5
We propose a cross-modal transformer-based neural correction models that refines the output of an automatic speech recognition (ASR) system so as to exclude ASR errors. Generally,…
Unified Autoregressive Modeling for Joint End-to-End Multi-Talker Overlapped Speech Recognition and Speaker Attribute Estimation
Ryo Masumura, Daiki Okamura, Naoki Makishima +4
In this paper, we present a novel modeling method for single-channel multi-talker overlapped automatic speech recognition (ASR) systems. Fully neural network based end-to-end model…
Enrollment-less training for personalized voice activity detection
Naoki Makishima, Mana Ihori, Tomohiro Tanaka +3
We present a novel personalized voice activity detection (PVAD) learning method that does not require enrollment data during training. PVAD is a task to detect the speech segments…
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks using Switching Tokens
Mana Ihori, Naoki Makishima, Tomohiro Tanaka +3
In this paper, we propose a novel spoken-text-style conversion method that can simultaneously execute multiple style conversion modules such as punctuation restoration and disfluen…
Audio-Visual Speech Separation Using Cross-Modal Correspondence Loss
Naoki Makishima, Mana Ihori, Akihiko Takashima +3
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 characteri…
End-to-End Automatic Speech Recognition with Deep Mutual Learning
Ryo Masumura, Mana Ihori, Akihiko Takashima +2
This paper is the first study to apply deep mutual learning (DML) to end-to-end ASR models. In DML, multiple models are trained simultaneously and collaboratively by mimicking each…
Large-Context Conversational Representation Learning: Self-Supervised Learning for Conversational Documents
Ryo Masumura, Naoki Makishima, Mana Ihori +3
This paper presents a novel self-supervised learning method for handling conversational documents consisting of transcribed text of human-to-human conversations. One of the key tec…
Hierarchical Transformer-based Large-Context End-to-end ASR with Large-Context Knowledge Distillation
Ryo Masumura, Naoki Makishima, Mana Ihori +3
We present a novel large-context end-to-end automatic speech recognition (E2E-ASR) model and its effective training method based on knowledge distillation. Common E2E-ASR models ha…
MAPGN: MAsked Pointer-Generator Network for sequence-to-sequence pre-training
Mana Ihori, Naoki Makishima, Tomohiro Tanaka +3
This paper presents a self-supervised learning method for pointer-generator networks to improve spoken-text normalization. Spoken-text normalization that converts spoken-style text…