Query Expansion System for the VoxCeleb Speaker Recognition Challenge
2020
- URL: http://arxiv.org/abs/2011.02882v1
- Date: Wed, 4 Nov 2020 05:24:18 GMT
- Title: Query Expansion System for the VoxCeleb Speaker Recognition Challenge
2020
- Authors: Yu-Sen Cheng, Chun-Liang Shih, Tien-Hong Lo, Wen-Ting Tseng, Berlin
Chen
- Abstract summary: We describe our submission to the VoxCeleb Speaker Recognition Challenge (VoxSRC) 2020.
One is to apply query expansion on speaker verification, which shows significant progress compared to baseline in the study.
Another is to combine its Probabilistic Linear Discriminant Analysis (PLDA) score with ResNet score.
- Score: 9.908371711364717
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this report, we describe our submission to the VoxCeleb Speaker
Recognition Challenge (VoxSRC) 2020. Two approaches are adopted. One is to
apply query expansion on speaker verification, which shows significant progress
compared to baseline in the study. Another is to use Kaldi extract x-vector and
to combine its Probabilistic Linear Discriminant Analysis (PLDA) score with
ResNet score.
Related papers
- Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications [18.151884620928936]
We present a novel study aiming to optimize the use of a Speaker-Attributed ASR (SA-ASR) system in real-life scenarios.
We propose a pipeline tailored to real-life applications involving Voice Activity Detection (VAD), Speaker Diarization (SD), and SA-ASR.
arXiv Detail & Related papers (2024-03-11T10:11:29Z) - DASA: Difficulty-Aware Semantic Augmentation for Speaker Verification [55.306583814017046]
We present a novel difficulty-aware semantic augmentation (DASA) approach for speaker verification.
DASA generates diversified training samples in speaker embedding space with negligible extra computing cost.
The best result achieves a 14.6% relative reduction in EER metric on CN-Celeb evaluation set.
arXiv Detail & Related papers (2023-10-18T17:07:05Z) - Exploring the Integration of Speech Separation and Recognition with
Self-Supervised Learning Representation [83.36685075570232]
This work provides an insightful investigation of speech separation in reverberant and noisy-reverberant scenarios as an ASR front-end.
We explore multi-channel separation methods, mask-based beamforming and complex spectral mapping, as well as the best features to use in the ASR back-end model.
A proposed integration using TF-GridNet-based complex spectral mapping and WavLM-based SSLR achieves a 2.5% word error rate in reverberant WHAMR! test set.
arXiv Detail & Related papers (2023-07-23T05:39:39Z) - VoxSRC 2022: The Fourth VoxCeleb Speaker Recognition Challenge [95.6159736804855]
The VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC-22) was held in conjunction with INTERSPEECH 2022.
The goal of this challenge was to evaluate how well state-of-the-art speaker recognition systems can diarise and recognise speakers from speech obtained "in the wild"
arXiv Detail & Related papers (2023-02-20T19:27:14Z) - The Newsbridge -Telecom SudParis VoxCeleb Speaker Recognition Challenge
2022 System Description [0.0]
We describe the system used by our team for the VoxCeleb Speaker Recognition Challenge 2022 (VoxSRC 2022) in the speaker diarization track.
Our solution was designed around a new combination of voice activity detection algorithms that uses the strengths of several systems.
arXiv Detail & Related papers (2023-01-17T15:52:39Z) - STC speaker recognition systems for the NIST SRE 2021 [56.05258832139496]
This paper presents a description of STC Ltd. systems submitted to the NIST 2021 Speaker Recognition Evaluation.
These systems consists of a number of diverse subsystems based on using deep neural networks as feature extractors.
For video modality we developed our best solution with RetinaFace face detector and deep ResNet face embeddings extractor trained on large face image datasets.
arXiv Detail & Related papers (2021-11-03T15:31:01Z) - The Phonexia VoxCeleb Speaker Recognition Challenge 2021 System
Description [1.3687617973585977]
We describe the Phonexia submission for the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21) in the unsupervised speaker verification track.
An embedding extractor was bootstrapped using momentum contrastive learning, with input augmentations as the only source of supervision.
A score fusion was done, by averaging the zt-normalized cosine scores of five different embedding extractors.
arXiv Detail & Related papers (2021-09-05T12:10:26Z) - Visualizing Classifier Adjacency Relations: A Case Study in Speaker
Verification and Voice Anti-Spoofing [72.4445825335561]
We propose a simple method to derive 2D representation from detection scores produced by an arbitrary set of binary classifiers.
Based upon rank correlations, our method facilitates a visual comparison of classifiers with arbitrary scores.
While the approach is fully versatile and can be applied to any detection task, we demonstrate the method using scores produced by automatic speaker verification and voice anti-spoofing systems.
arXiv Detail & Related papers (2021-06-11T13:03:33Z) - VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challenge [99.82500204110015]
We held the second installment of the VoxCeleb Speaker Recognition Challenge in conjunction with Interspeech 2020.
The goal of this challenge was to assess how well current speaker recognition technology is able to diarise and recognize speakers in unconstrained or in the wild' data.
This paper outlines the challenge, and describes the baselines, methods used, and results.
arXiv Detail & Related papers (2020-12-12T17:20:57Z) - Tongji University Undergraduate Team for the VoxCeleb Speaker
Recognition Challenge2020 [10.836635938778684]
We applied the RSBU-CW module to the ResNet34 framework to improve the denoising ability of the network.
We trained two variants of ResNet,used score fusion and data-augmentation methods to improve the performance of the model.
arXiv Detail & Related papers (2020-10-20T09:25:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.