Paola García | DeepAI

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∙ 06/23/2023

The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios

The CHiME challenges have played a significant role in the development a...

0 Samuele Cornell, et al. ∙

∙ 11/06/2022

Bridging Speech and Textual Pre-trained Models with Unsupervised ASR

Spoken language understanding (SLU) is a task aiming to extract high-lev...

0 Jiatong Shi, et al. ∙

∙ 11/01/2022

Adapting self-supervised models to multi-talker speech recognition using speaker embeddings

Self-supervised learning (SSL) methods which learn representations of da...

0 Zili Huang, et al. ∙

∙ 10/13/2022

On Compressing Sequences for Self-Supervised Speech Models

Compressing self-supervised models has become increasingly necessary, as...

0 Yen Meng, et al. ∙

∙ 10/07/2022

Mutual Learning of Single- and Multi-Channel End-to-End Neural Diarization

Due to the high performance of multi-channel speech processing, we can u...

0 Shota Horiguchi, et al. ∙

∙ 06/06/2022

Online Neural Diarization of Unlimited Numbers of Speakers

A method to perform offline and online speaker diarization for an unlimi...

0 Shota Horiguchi, et al. ∙

∙ 10/10/2021

Multi-Channel End-to-End Neural Diarization with Distributed Microphones

Recent progress on end-to-end neural diarization (EEND) has enabled over...

0 Shota Horiguchi, et al. ∙

∙ 07/04/2021

Towards Neural Diarization for Unlimited Numbers of Speakers Using Global and Local Attractors

Attractor-based end-to-end diarization is achieving comparable accuracy ...

0 Shota Horiguchi, et al. ∙

∙ 06/20/2021

Encoder-Decoder Based Attractor Calculation for End-to-End Neural Diarization

This paper investigates an end-to-end neural diarization (EEND) method f...

0 Shota Horiguchi, et al. ∙

∙ 06/09/2021

Semi-Supervised Training with Pseudo-Labeling for End-to-End Neural Diarization

In this paper, we present a semi-supervised training technique using pse...

0 Yuki Takashima, et al. ∙

∙ 06/08/2021

End-to-End Speaker Diarization Conditioned on Speech Activity and Overlap Detection

In this paper, we present a conditional multitask learning method for en...

0 Yuki Takashima, et al. ∙

∙ 02/02/2021

The Hitachi-JHU DIHARD III System: Competitive End-to-End Neural Diarization and X-Vector Clustering Systems Combined by DOVER-Lap

This paper provides a detailed description of the Hitachi-JHU system tha...

15 Shota Horiguchi, et al. ∙

∙ 01/21/2021

Online End-to-End Neural Diarization Handling Overlapping Speech and Flexible Numbers of Speakers

This paper proposes an online end-to-end diarization that can handle ove...

0 Yawen Xue, et al. ∙

∙ 12/18/2020

End-to-End Speaker Diarization as Post-Processing

This paper investigates the utilization of an end-to-end diarization mod...

0 Shota Horiguchi, et al. ∙

∙ 06/14/2020

The JHU Multi-Microphone Multi-Speaker ASR System for the CHiME-6 Challenge

This paper summarizes the JHU team's efforts in tracks 1 and 2 of the CH...

0 Ashish Arora, et al. ∙

∙ 02/14/2020

Speaker Diarization with Region Proposal Network

Speaker diarization is an important pre-processing step for many speech ...

0 Zili Huang, et al. ∙

∙ 12/02/2019

Speaker detection in the wild: Lessons learned from JSALT 2019

This paper presents the problems and solutions addressed at the JSALT wo...

0 Paola García, et al. ∙

∙ 10/25/2019

Feature Enhancement with Deep Feature Losses for Speaker Verification

Speaker Verification still suffers from the challenge of generalization ...

0 Saurabh Kataria, et al. ∙

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