61 citations · 253 across the 25 of their papers we have counts for
26 papers
Acoustic modeling for Overlapping Speech Recognition: JHU Chime-5 Challenge System
Vimal Manohar, Szu-Jui Chen, Zhiqi Wang +3
This paper summarizes our acoustic modeling efforts in the Johns Hopkins University speech recognition system for the CHiME-5 challenge to recognize highly-overlapped dinner party…
Audio Difference Learning for Audio Captioning
Tatsuya Komatsu, Yusuke Fujita, Kazuya Takeda +1
This study introduces a novel training paradigm, audio difference learning, for improving audio captioning. The fundamental concept of the proposed learning method is to create a f…
Neural Diarization with Non-autoregressive Intermediate Attractors
Yusuke Fujita, Tatsuya Komatsu, Robin Scheibler +2
End-to-end neural diarization (EEND) with encoder-decoder-based attractors (EDA) is a promising method to handle the whole speaker diarization problem simultaneously with a single…
Better Intermediates Improve CTC Inference
Tatsuya Komatsu, Yusuke Fujita, Jaesong Lee +3
This paper proposes a method for improved CTC inference with searched intermediates and multi-pass conditioning. The paper first formulates self-conditioned CTC as a probabilistic…
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR
Yusuke Fujita, Tatsuya Komatsu, Yusuke Kida
End-to-end automatic speech recognition directly maps input speech to characters. However, the mapping can be problematic when several different pronunciations should be mapped int…
InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR
Yu Nakagome, Tatsuya Komatsu, Yusuke Fujita +2
This paper proposes InterAug: a novel training method for CTC-based ASR using augmented intermediate representations for conditioning. The proposed method exploits the conditioning…
Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization
Yuki Takashima, Yusuke Fujita, Shota Horiguchi +3
In this paper, we present a semi-supervised training technique using pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has shown promising performance compa…
End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection
Yuki Takashima, Yusuke Fujita, Shinji Watanabe +3
In this paper, we present a conditional multitask learning method for end-to-end neural speaker diarization (EEND). The EEND system has shown promising performance compared with tr…
Encoder-Decoder Based Attractors for End-to-End Neural Diarization
Shota Horiguchi, Yusuke Fujita, Shinji Watanabe +2
This paper investigates an end-to-end neural diarization (EEND) method for an unknown number of speakers. In contrast to the conventional cascaded approach to speaker diarization,…
The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap
Shota Horiguchi, Nelson Yalta, Paola Garcia +7
This paper provides a detailed description of the Hitachi-JHU system that was submitted to the Third DIHARD Speech Diarization Challenge. The system outputs the ensemble results of…
Online Streaming End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers
Yawen Xue, Shota Horiguchi, Yusuke Fujita +4
We propose a streaming diarization method based on an end-to-end neural diarization (EEND) model, which handles flexible numbers of speakers and overlapping speech. In our previous…
End-to-End Speaker Diarization as Post-Processing
Shota Horiguchi, Paola Garcia, Yusuke Fujita +2
This paper investigates the utilization of an end-to-end diarization model as post-processing of conventional clustering-based diarization. Clustering-based diarization methods par…
Block-Online Guided Source Separation
Shota Horiguchi, Yusuke Fujita, Kenji Nagamatsu
We propose a block-online algorithm of guided source separation (GSS). GSS is a speech separation method that uses diarization information to update parameters of the generative mo…
Utterance-Wise Meeting Transcription System Using Asynchronous Distributed Microphones
Shota Horiguchi, Yusuke Fujita, Kenji Nagamatsu
A novel framework for meeting transcription using asynchronous microphones is proposed in this paper. It consists of audio synchronization, speaker diarization, utterance-wise spee…
Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals
Jing Shi, Xuankai Chang, Pengcheng Guo +5
Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus…
Speaker-Conditional Chain Model for Speech Separation and Extraction
Jing Shi, Jiaming Xu, Yusuke Fujita +2
Speech separation has been extensively explored to tackle the cocktail party problem. However, these studies are still far from having enough generalization capabilities for real s…
Neural Speaker Diarization with Speaker-Wise Chain Rule
Yusuke Fujita, Shinji Watanabe, Shota Horiguchi +3
Speaker diarization is an essential step for processing multi-speaker audio. Although an end-to-end neural diarization (EEND) method achieved state-of-the-art performance, it is li…
Online End-to-End Neural Diarization with Speaker-Tracing Buffer
Yawen Xue, Shota Horiguchi, Yusuke Fujita +2
This paper proposes a novel online speaker diarization algorithm based on a fully supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a speaker's…
End-to-End Speaker Diarization for an Unknown Number of Speakers with Encoder-Decoder Based Attractors
Shota Horiguchi, Yusuke Fujita, Shinji Watanabe +2
End-to-end speaker diarization for an unknown number of speakers is addressed in this paper. Recently proposed end-to-end speaker diarization outperformed conventional clustering-b…
End-to-End Neural Diarization: Reformulating Speaker Diarization as Simple Multi-label Classification
Yusuke Fujita, Shinji Watanabe, Shota Horiguchi +2
The most common approach to speaker diarization is clustering of speaker embeddings. However, the clustering-based approach has a number of problems; i.e., (i) it is not optimized…
Speaker Diarization with Region Proposal Network
Zili Huang, Shinji Watanabe, Yusuke Fujita +4
Speaker diarization is an important pre-processing step for many speech applications, and it aims to solve the "who spoke when" problem. Although the standard diarization systems c…
Simultaneous Speech Recognition and Speaker Diarization for Monaural Dialogue Recordings with Target-Speaker Acoustic Models
Naoyuki Kanda, Shota Horiguchi, Yusuke Fujita +3
This paper investigates the use of target-speaker automatic speech recognition (TS-ASR) for simultaneous speech recognition and speaker diarization of single-channel dialogue recor…
End-to-End Neural Speaker Diarization with Self-attention
Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi +3
Speaker diarization has been mainly developed based on the clustering of speaker embeddings. However, the clustering-based approach has two major problems; i.e., (i) it is not opti…
End-to-End Neural Speaker Diarization with Permutation-Free Objectives
Yusuke Fujita, Naoyuki Kanda, Shota Horiguchi +2
In this paper, we propose a novel end-to-end neural-network-based speaker diarization method. Unlike most existing methods, our proposed method does not have separate modules for e…