1 citations · 4 across the 22 of their papers we have counts for
22 papers
MV2GF: Multi-view Pedestrian Detection with a Visual Geometric Foundation Model
Taiga Yamane, Satoshi Suzuki, Ryo Masumura +4
Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view map from multi-view images. Recent MVPD methods adopt a unified framework that pr…
Difference Vector Equalization for Robust Fine-tuning of Vision-Language Models
Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda +7
Contrastive pre-trained vision-language models, such as CLIP, demonstrate strong generalization abilities in zero-shot classification by leveraging embeddings extracted from image…
Joint Modeling of Big Five and HEXACO for Multimodal Apparent Personality-trait Recognition
Ryo Masumura, Shota Orihashi, Mana Ihori +6
This paper proposes a joint modeling method of the Big Five, which has long been studied, and HEXACO, which has recently attracted attention in psychology, for automatically recogn…
Few-shot Personalization via In-Context Learning for Speech Emotion Recognition based on Speech-Language Model
Mana Ihori, Taiga Yamane, Naotaka Kawata +5
This paper proposes a personalization method for speech emotion recognition (SER) through in-context learning (ICL). Since the expression of emotions varies from person to person,…
MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection
Taiga Yamane, Satoshi Suzuki, Ryo Masumura +5
Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view (BEV) from multi-view images. In MVPD, end-to-end trainable deep learning methods…
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…
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…
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…
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…
Memory Attentive Fusion: External Language Model Integration for Transformer-based Sequence-to-Sequence Model
Mana Ihori, Ryo Masumura, Naoki Makishima +3
This paper presents a novel fusion method for integrating an external language model (LM) into the Transformer based sequence-to-sequence (seq2seq) model. While paired data are bas…