120 citations · 452 across the 91 of their papers we have counts for
17 papers · 1 filter
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition
Atsunori Ogawa, Naohiro Tawara, Takatomo Kano +1
Confidence estimation, in which we estimate the reliability of each recognized token (e.g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and dete…
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models
Atsunori Ogawa, Naohiro Tawara, Marc Delcroix +1
We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses. Previou…
How does end-to-end speech recognition training impact speech enhancement artifacts?
Kazuma Iwamoto, Tsubasa Ochiai, Marc Delcroix +4
Jointly training a speech enhancement (SE) front-end and an automatic speech recognition (ASR) back-end has been investigated as a way to mitigate the influence of \emph{processing…
Neural network-based virtual microphone estimation with virtual microphone and beamformer-level multi-task loss
Hanako Segawa, Tsubasa Ochiai, Marc Delcroix +5
Array processing performance depends on the number of microphones available. Virtual microphone estimation (VME) has been proposed to increase the number of microphone signals arti…
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition
Atsunori Ogawa, Takafumi Moriya, Naoyuki Kamo +2
We propose a new shallow fusion (SF) method to exploit an external backward language model (BLM) for end-to-end automatic speech recognition (ASR). The BLM has complementary charac…
Discriminative Training of VBx Diarization
Dominik Klement, Mireia Diez, Federico Landini +4
Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a…
NTT speaker diarization system for CHiME-7: multi-domain, multi-microphone End-to-end and vector clustering diarization
Naohiro Tawara, Marc Delcroix, Atsushi Ando +1
This paper details our speaker diarization system designed for multi-domain, multi-microphone casual conversations. The proposed diarization pipeline uses weighted prediction error…
Meeting Recognition with Continuous Speech Separation and Transcription-Supported Diarization
Thilo von Neumann, Christoph Boeddeker, Tobias Cord-Landwehr +2
We propose a modular pipeline for the single-channel separation, recognition, and diarization of meeting-style recordings and evaluate it on the Libri-CSS dataset. Using a Continuo…
Target Speech Extraction with Conditional Diffusion Model
Naoyuki Kamo, Marc Delcroix, Tomohiro Nakatani
Diffusion model-based speech enhancement has received increased attention since it can generate very natural enhanced signals and generalizes well to unseen conditions. Diffusion m…
MeetEval: A Toolkit for Computation of Word Error Rates for Meeting Transcription Systems
Thilo von Neumann, Christoph Boeddeker, Marc Delcroix +1
MeetEval is an open-source toolkit to evaluate all kinds of meeting transcription systems. It provides a unified interface for the computation of commonly used Word Error Rates (WE…
Transfer Learning from Pre-trained Language Models Improves End-to-End Speech Summarization
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
End-to-end speech summarization (E2E SSum) directly summarizes input speech into easy-to-read short sentences with a single model. This approach is promising because it, in contras…
SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?
Takanori Ashihara, Takafumi Moriya, Kohei Matsuura +5
Self-supervised learning (SSL) for speech representation has been successfully applied in various downstream tasks, such as speech and speaker recognition. More recently, speech SS…
Knowledge Distillation for Neural Transducer-based Target-Speaker ASR: Exploiting Parallel Mixture/Single-Talker Speech Data
Takafumi Moriya, Hiroshi Sato, Tsubasa Ochiai +7
Neural transducer (RNNT)-based target-speaker speech recognition (TS-RNNT) directly transcribes a target speaker's voice from a multi-talker mixture. It is a promising approach for…
Downstream Task Agnostic Speech Enhancement with Self-Supervised Representation Loss
Hiroshi Sato, Ryo Masumura, Tsubasa Ochiai +8
Self-supervised learning (SSL) is the latest breakthrough in speech processing, especially for label-scarce downstream tasks by leveraging massive unlabeled audio data. The noise r…
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Marc Delcroix, Naohiro Tawara, Mireia Diez +6
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods. EEND-VC esti…
Leveraging Large Text Corpora for End-to-End Speech Summarization
Kohei Matsuura, Takanori Ashihara, Takafumi Moriya +4
End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech re…
Neural Target Speech Extraction: An Overview
Katerina Zmolikova, Marc Delcroix, Tsubasa Ochiai +3
Humans can listen to a target speaker even in challenging acoustic conditions that have noise, reverberation, and interfering speakers. This phenomenon is known as the cocktail-par…