Search Results for author: Xuechen Liu

Found 16 papers, 3 papers with code

Generalizing Speaker Verification for Spoof Awareness in the Embedding Space

no code implementations20 Jan 2024 Xuechen Liu, Md Sahidullah, Kong Aik Lee, Tomi Kinnunen

To this end, we propose to generalize the standalone ASV (G-SASV) against spoofing attacks, where we leverage limited training data from CM to enhance a simple backend in the embedding space, without the involvement of a separate CM module during the test (authentication) phase.

Domain Adaptation Speaker Verification

Towards single integrated spoofing-aware speaker verification embeddings

1 code implementation30 May 2023 Sung Hwan Mun, Hye-jin Shim, Hemlata Tak, Xin Wang, Xuechen Liu, Md Sahidullah, Myeonghun Jeong, Min Hyun Han, Massimiliano Todisco, Kong Aik Lee, Junichi Yamagishi, Nicholas Evans, Tomi Kinnunen, Nam Soo Kim, Jee-weon Jung

Second, competitive performance should be demonstrated compared to the fusion of automatic speaker verification (ASV) and countermeasure (CM) embeddings, which outperformed single embedding solutions by a large margin in the SASV2022 challenge.

Speaker Verification

Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification

no code implementations2 Mar 2023 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Even though deep speaker models have demonstrated impressive accuracy in speaker verification tasks, this often comes at the expense of increased model size and computation time, presenting challenges for deployment in resource-constrained environments.

Knowledge Distillation Speaker Verification

Baselines and Protocols for Household Speaker Recognition

1 code implementation30 Apr 2022 Alexey Sholokhov, Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short utterances, (iii) possibly absent speaker labels of the enrollment data (passive enrollment), and (iv) presence of unknown persons (guests).

Speaker Recognition

Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation

no code implementations21 Mar 2022 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

In this paper, we initiate the concern of enhancing the spoofing robustness of the automatic speaker verification (ASV) system, without the primary presence of a separate countermeasure module.

Speaker Verification Unsupervised Domain Adaptation

Learnable Nonlinear Compression for Robust Speaker Verification

no code implementations10 Feb 2022 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

We consider different kinds of channel-dependent (CD) nonlinear compression methods optimized in a data-driven manner.

Speaker Verification

Optimizing Multi-Taper Features for Deep Speaker Verification

no code implementations21 Oct 2021 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Multi-taper estimators provide low-variance power spectrum estimates that can be used in place of the windowed discrete Fourier transform (DFT) to extract speech features such as mel-frequency cepstral coefficients (MFCCs).

Open-Ended Question Answering Speaker Verification

Parameterized Channel Normalization for Far-field Deep Speaker Verification

no code implementations24 Sep 2021 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

We address far-field speaker verification with deep neural network (DNN) based speaker embedding extractor, where mismatch between enrollment and test data often comes from convolutive effects (e. g. room reverberation) and noise.

Speaker Verification

Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification

no code implementations24 Sep 2021 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification.

Robust Speech Recognition Speaker Verification +1

ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan

1 code implementation1 Sep 2021 Héctor Delgado, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee, Xuechen Liu, Andreas Nautsch, Jose Patino, Md Sahidullah, Massimiliano Todisco, Xin Wang, Junichi Yamagishi

The automatic speaker verification spoofing and countermeasures (ASVspoof) challenge series is a community-led initiative which aims to promote the consideration of spoofing and the development of countermeasures.

Face Swapping Speaker Verification

ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

no code implementations1 Sep 2021 Junichi Yamagishi, Xin Wang, Massimiliano Todisco, Md Sahidullah, Jose Patino, Andreas Nautsch, Xuechen Liu, Kong Aik Lee, Tomi Kinnunen, Nicholas Evans, Héctor Delgado

In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection.

Face Swapping Speaker Verification

Learnable MFCCs for Speaker Verification

no code implementations20 Feb 2021 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

We propose a learnable mel-frequency cepstral coefficient (MFCC) frontend architecture for deep neural network (DNN) based automatic speaker verification.

Speaker Verification

A Comparative Re-Assessment of Feature Extractors for Deep Speaker Embeddings

no code implementations30 Jul 2020 Xuechen Liu, Md Sahidullah, Tomi Kinnunen

Modern automatic speaker verification relies largely on deep neural networks (DNNs) trained on mel-frequency cepstral coefficient (MFCC) features.

Speaker Verification

UIAI System for Short-Duration Speaker Verification Challenge 2020

no code implementations26 Jul 2020 Md Sahidullah, Achintya Kumar Sarkar, Ville Vestman, Xuechen Liu, Romain Serizel, Tomi Kinnunen, Zheng-Hua Tan, Emmanuel Vincent

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.

Text-Dependent Speaker Verification

AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale

no code implementations31 Aug 2018 Jiayu Du, Xingyu Na, Xuechen Liu, Hui Bu

For research community, we hope that AISHELL-2 corpus can be a solid resource for topics like transfer learning and robust ASR.

Chinese Word Segmentation speech-recognition +2

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