UIAI System for Short-Duration Speaker Verification Challenge 2020

• URL: http://arxiv.org/abs/2007.13118v1
• Date: Sun, 26 Jul 2020 12:32:34 GMT
• Title: UIAI System for Short-Duration Speaker Verification Challenge 2020
• Abstract summary: We present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. We investigate different feature extraction and modeling approaches for automatic speaker verification (ASV) and utterance verification (UV) Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0.072 and an equal error rate (EER) of 2.14%.
• Score: 45.816875527541065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we present the system description of the UIAI entry for the short-duration speaker verification (SdSV) challenge 2020. Our focus is on Task 1 dedicated to text-dependent speaker verification. We investigate different feature extraction and modeling approaches for automatic speaker verification (ASV) and utterance verification (UV). We have also studied different fusion strategies for combining UV and ASV modules. Our primary submission to the challenge is the fusion of seven subsystems which yields a normalized minimum detection cost function (minDCF) of 0.072 and an equal error rate (EER) of 2.14% on the evaluation set. The single system consisting of a pass-phrase identification based model with phone-discriminative bottleneck features gives a normalized minDCF of 0.118 and achieves 19% relative improvement over the state-of-the-art challenge baseline.

Related papers

• Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report [0.0]
  The system achieved a Minimum Detection Cost Function (MinDCF) of 0.0461 and an Equal Error Rate (EER) of 1.3%.<n>Our approach focused on adapting existing state-of-the-art neural networks, ResNet-TDNN and NeXt-TDNN, originally trained on the VoxCeleb dataset.
  arXiv  Detail & Related papers  (2026-05-14T14:38:11Z)
• Neural Sentinel: Unified Vision Language Model (VLM) for License Plate Recognition with Human-in-the-Loop Continual Learning [0.0]
  This research presents Neural Sentinel, a novel unified approach that attributes license plate recognition, state classification, and vehicle extraction through a single forward pass.<n>Our primary contribution lies in demonstrating that a fine-tuned PaliGemma 3B model, adapted via Low-Rank Adaptation (LoRA), can simultaneously answer multiple visual questions about vehicle images.<n>The system achieves a mean inference latency of 152ms with an Expected Error (ECE) of 0.048, indicating well confidence estimates.
  arXiv  Detail & Related papers  (2026-02-04T16:04:15Z)
• Recent Trends in Distant Conversational Speech Recognition: A Review of CHiME-7 and 8 DASR Challenges [63.741916531380696]
  The CHiME-7 and 8 distant speech recognition (DASR) challenges focus on multi-channel, generalizable, joint automatic speech recognition (ASR) and diarization of conversational speech.<n>This paper outlines the challenges' design, evaluation metrics, datasets, and baseline systems while analyzing key trends from participant submissions.
  arXiv  Detail & Related papers  (2025-07-24T07:56:24Z)
• DIN-CTS: Low-Complexity Depthwise-Inception Neural Network with Contrastive Training Strategy for Deepfake Speech Detection [35.92231189181208]
  We propose a deep neural network approach for deepfake speech detection (DSD) based on a lowcomplexity Depthwise-Inception Network (DIN) trained with a contrastive training strategy (CTS) To evaluate our proposed systems, we conducted extensive experiments on the benchmark dataset of ASVspoof 2019 LA. Our proposed system outperforms the single-system submissions in the ASVspoof 2019 LA challenge, showcasing its potential for real-time applications.
  arXiv  Detail & Related papers  (2025-02-27T16:09:04Z)
• The SVASR System for Text-dependent Speaker Verification (TdSV) AAIC Challenge 2024 [0.0]
  The proposed system incorporates a Fast-Conformer-based ASR module to validate speech content. For speaker verification, we propose a feature fusion approach that combines speaker embeddings extracted from wav2vec-BERT and ReNet models.
  arXiv  Detail & Related papers  (2024-11-25T10:53:45Z)
• Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024 [8.940008511570207]
  This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) The rapid advancement of generative AI models presents significant challenges for detecting AI-generated deepfake singing voices. The Singing Voice Deepfake Detection (SVDD) Challenge 2024 aims to address this complex task.
  arXiv  Detail & Related papers  (2024-09-03T21:28:45Z)
• USTC-KXDIGIT System Description for ASVspoof5 Challenge [30.962424920219224]
  This paper describes the USTC-KXDIGIT system submitted to the ASVspoof5 Challenge for Track 1 (speech deepfake detection) and Track 2 (spoofing- automatic speaker verification, SASV) Track 1 showcases a diverse range of technical qualities from potential processing algorithms and includes both open and closed conditions. In Track 2, we continued using the CM system from Track 1 and fused it with a CNN-based ASV system. This approach achieved 0.2814 min-aDCF in the closed condition and 0.0756 min-aDCF in the open condition, showcasing superior performance in
  arXiv  Detail & Related papers  (2024-09-03T08:28:58Z)
• OV-DINO: Unified Open-Vocabulary Detection with Language-Aware Selective Fusion [88.59397418187226]
  We propose a novel unified open-vocabulary detection method called OV-DINO. It is pre-trained on diverse large-scale datasets with language-aware selective fusion in a unified framework. We evaluate the performance of the proposed OV-DINO on popular open-vocabulary detection benchmarks.
  arXiv  Detail & Related papers  (2024-07-10T17:05:49Z)
• MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition [62.89464258519723]
  We propose a multi-layer cross-attention fusion based AVSR approach that promotes representation of each modality by fusing them at different levels of audio/visual encoders. Our proposed approach surpasses the first-place system, establishing a new SOTA cpCER of 29.13% on this dataset.
  arXiv  Detail & Related papers  (2024-01-07T08:59:32Z)
• An Inception-Residual-Based Architecture with Multi-Objective Loss for Detecting Respiratory Anomalies [10.29057783664056]
  This paper presents a deep learning system applied for detecting anomalies from respiratory sound recordings. Our proposed system integrates Inception-residual-based backbone models combined with multi-head attention and multi-objective loss to classify respiratory anomalies.
  arXiv  Detail & Related papers  (2023-03-07T18:10:05Z)
• Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion [88.34134732217416]
  This work focuses on fusion-based SASV solutions and proposes a multi-model fusion framework to leverage the power of multiple state-of-the-art ASV and CM models. The proposed framework vastly improves the SASV-EER from 8.75% to 1.17%, which is 86% relative improvement compared to the best baseline system in the SASV challenge.
  arXiv  Detail & Related papers  (2022-06-18T06:41:06Z)
• End-to-End Zero-Shot HOI Detection via Vision and Language Knowledge Distillation [86.41437210485932]
  We aim at advancing zero-shot HOI detection to detect both seen and unseen HOIs simultaneously. We propose a novel end-to-end zero-shot HOI Detection framework via vision-language knowledge distillation. Our method outperforms the previous SOTA by 8.92% on unseen mAP and 10.18% on overall mAP.
  arXiv  Detail & Related papers  (2022-04-01T07:27:19Z)
• Joint Contextual Modeling for ASR Correction and Language Understanding [60.230013453699975]
  We propose multi-task neural approaches to perform contextual language correction on ASR outputs jointly with language understanding (LU) We show that the error rates of off the shelf ASR and following LU systems can be reduced significantly by 14% relative with joint models trained using small amounts of in-domain data.
  arXiv  Detail & Related papers  (2020-01-28T22:09:25Z)

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.