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Discriminative speaker embedding with serialized multi-layer multi-head attention

https://doi.org/10.1016/j.specom.2022.09.003

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/j.csl.2019.101027openalex-snapshot

Voxceleb: Large-scale speaker verification in the wild

inferredcites →https://doi.org/10.1016/j.csl.2019.101033openalex-snapshot

NEC-TT System for Mixed-Bandwidth and Multi-Domain Speaker Recognition

inferredcites →https://doi.org/10.1016/j.csl.2019.101058openalex-snapshot

Two decades into Speaker Recognition Evaluation - are we there yet?

inferredcites →https://doi.org/10.1016/j.softx.2021.100697openalex-snapshot

ASVtorch toolkit: Speaker verification with deep neural networks

inferredcites →https://doi.org/10.1016/j.specom.2022.01.002openalex-snapshot

CN-Celeb: Multi-genre speaker recognition

inferredcites →https://doi.org/10.1109/29.21701openalex-snapshot

Phoneme recognition using time-delay neural networks

inferredcites →https://doi.org/10.1109/cvpr.2016.10openalex-snapshot

Stacked Attention Networks for Image Question Answering

inferredcites →https://doi.org/10.1109/cvpr.2017.683openalex-snapshot

Residual Attention Network for Image Classification

inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-snapshot

Non-local Neural Networks

inferredcites →https://doi.org/10.1109/eusipco36177.2015openalex-snapshot

2015 23rd European Signal Processing Conference (EUSIPCO)

inferredcites →https://doi.org/10.1109/icassp17257.2015openalex-snapshot

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

inferredcites →https://doi.org/10.1109/icassp33580.2016openalex-snapshot

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

inferredcites →https://doi.org/10.1109/icassp34228.2018openalex-snapshot

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

inferredcites →https://doi.org/10.1109/icassp39728.2021openalex-snapshot

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

inferredcites →https://doi.org/10.1109/icassp40776.2020openalex-snapshot

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

inferredcites →https://doi.org/10.1109/icassp43922.2022openalex-snapshot

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

inferredcites →https://doi.org/10.1109/iscslp43912.2018openalex-snapshot

2018 11th International Symposium on Chinese Spoken Language Processing (ISCSLP)

inferredcites →https://doi.org/10.1109/lsp.2006.870086openalex-snapshot

Support vector machines using GMM supervectors for speaker verification

inferredcites →https://doi.org/10.1109/slt37674.2016openalex-snapshot

2016 IEEE Spoken Language Technology Workshop (SLT)

inferredcites →https://doi.org/10.1109/slt43100.2018openalex-snapshot

2018 IEEE Spoken Language Technology Workshop (SLT)

inferredcites →https://doi.org/10.1109/tasl.2010.2064307openalex-snapshot

Front-End Factor Analysis for Speaker Verification

inferredcites →https://doi.org/10.1109/tnnls.2020.3019893openalex-snapshot

Attention in Natural Language Processing

inferredcites →https://doi.org/10.18653/v1/n16-1174openalex-snapshot

Hierarchical Attention Networks for Document Classification

inferredcites →https://doi.org/10.21437/interspeech.2017-950openalex-snapshot

VoxCeleb: A Large-Scale Speaker Identification Dataset

inferredcites →https://doi.org/10.21437/interspeech.2018-1910openalex-snapshot

Self-Attentional Acoustic Models

inferredcites →https://doi.org/10.21437/interspeech.2018-1929openalex-snapshot

VoxCeleb2: Deep Speaker Recognition

inferredcites →https://doi.org/10.21437/interspeech.2018-993openalex-snapshot

Attentive Statistics Pooling for Deep Speaker Embedding

inferredcites →https://doi.org/10.21437/interspeech.2019-2616openalex-snapshot

Self Multi-Head Attention for Speaker Recognition

inferredcites →https://doi.org/10.21437/interspeech.2020-1446openalex-snapshot

Self-Attention Encoding and Pooling for Speaker Recognition

inferredcites →https://doi.org/10.21437/interspeech.2020-3015openalex-snapshot

Conformer: Convolution-augmented Transformer for Speech Recognition

inferredcites →https://doi.org/10.21437/interspeech.2021-2210openalex-snapshot

Serialized Multi-Layer Multi-Head Attention for Neural Speaker Embedding

inferredcites →https://doi.org/10.21437/odyssey.2018-53openalex-snapshot

On deep speaker embeddings for text-independent speaker recognition

inferredcites →https://doi.org/10.4230/lipics.cosit.2022.18openalex-snapshot

AI-Assisted Pipeline for Dynamic Generation of Trustworthy Health Supplement Content at Scale

inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot

Exploiting Generative AI to Scale up Intelligent Tutoring Systems

inferredcites →https://doi.org/10.48550/arxiv.1409.0473openalex-snapshot

Neural Machine Translation by Jointly Learning to Align and Translate

inferredcites →https://doi.org/10.48550/arxiv.1609.03499openalex-snapshot

WaveNet: A Generative Model for Raw Audio

inferredcites →https://doi.org/10.48550/arxiv.1703.03130openalex-snapshot

A Structured Self-attentive Sentence Embedding

inferredcites →https://doi.org/10.48550/arxiv.1705.02304openalex-snapshot

Deep Speaker: an End-to-End Neural Speaker Embedding System

inferredcites →https://doi.org/10.48550/arxiv.1711.05101openalex-snapshot

Decoupled Weight Decay Regularization

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4673657openalex-extracted

inferred← citeshttps://doi.org/10.1007/978-981-95-1525-7_9openalex-snapshot

A Low-Consumption Teaching Role Recognition Method for Teaching Analysis

inferred← citeshttps://doi.org/10.1007/978-981-96-0125-7_31openalex-snapshot

Integrating Voice Activity Detection to Enhance Robustness of On-Device Speaker Verification

inferred← citeshttps://doi.org/10.1007/s10489-023-04953-2openalex-snapshot

TMS: Temporal multi-scale in time-delay neural network for speaker verification

inferred← citeshttps://doi.org/10.1007/s10772-026-10269-6openalex-snapshot

Multi-scale and cardinality-based backbone: toward learning a general-purpose speaker representation

inferred← citeshttps://doi.org/10.1007/s11036-025-02442-zopenalex-snapshot

Parallel Recommendation for Multi Interactive Resources in Mobile Networks Based on Label Attributes and Behavior Sequence

inferred← citeshttps://doi.org/10.1007/s11370-024-00537-2openalex-snapshot

MAP3F: a decentralized approach to multi-agent pathfinding and collision avoidance with scalable 1D, 2D, and 3D feature fusion

inferred← citeshttps://doi.org/10.1016/j.aej.2022.12.009openalex-snapshot

Real-time driver distraction recognition: A hybrid genetic deep network based approach

inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109312openalex-snapshot

Deep attentive adaptive filter module in residual blocks for text-independent speaker verification

inferred← citeshttps://doi.org/10.1016/j.neucom.2025.130429openalex-snapshot

ConFusionformer: Locality-enhanced Conformer through multi-resolution attention fusion for speaker verification

inferred← citeshttps://doi.org/10.1016/j.specom.2023.102991openalex-snapshot

Graph attention-based deep embedded clustering for speaker diarization

inferred← citeshttps://doi.org/10.1038/s41598-025-13176-zopenalex-snapshot

Compressive strength modelling of cenosphere and copper slag-based geopolymer concrete using deep learning model

inferred← citeshttps://doi.org/10.1109/icassp49357.2023.10096230openalex-snapshot

Discriminative Speaker Representation Via Contrastive Learning with Class-Aware Attention in Angular Space

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Compressive Strength Prediction of Fly Ash-Based Concrete Using Single and Hybrid Machine Learning Models

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Voiceprint Recognition under Cross-Scenario Conditions Using Perceptual Wavelet Packet Entropy-Guided Efficient-Channel-Attention–Res2Net–Time-Delay-Neural-Network Model

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