43 citations · 167 across the 19 of their papers we have counts for
19 papers
Team Hitachi @ AutoMin 2021: Reference-free Automatic Minuting Pipeline with Argument Structure Construction over Topic-based Summarization
Atsuki Yamaguchi, Gaku Morio, Hiroaki Ozaki +2
This paper introduces the proposed automatic minuting system of the Hitachi team for the First Shared Task on Automatic Minuting (AutoMin-2021). We utilize a reference-free approac…
Emotional Speech Synthesis for Companion Robot to Imitate Professional Caregiver Speech
Takeshi Homma, Qinghua Sun, Takuya Fujioka +5
When people try to influence others to do something, they subconsciously adjust their speech to include appropriate emotional information. In order for a robot to influence people…
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…
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…
Building Multi lingual TTS using Cross Lingual Voice Conversion
Qinghua Sun, Kenji Nagamatsu
In this paper we propose a new cross-lingual Voice Conversion (VC) approach which can generate all speech parameters (MCEP, LF0, BAP) from one DNN model using PPGs (Phonetic Poster…
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…
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…
Addressing Ambiguity of Emotion Labels Through Meta-Learning
Takuya Fujioka, Dario Bertero, Takeshi Homma +1
Emotion labels in emotion recognition corpora are highly noisy and ambiguous, due to the annotators' subjective perception of emotions. Such ambiguity may introduce errors in autom…
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…
Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition
Naoyuki Kanda, Shota Horiguchi, Ryoichi Takashima +3
In this paper, we propose a novel auxiliary loss function for target-speaker automatic speech recognition (ASR). Our method automatically extracts and transcribes target speaker's…
Guided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
Naoyuki Kanda, Christoph Boeddeker, Jens Heitkaemper +4
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for…