175 citations · 709 across the 60 of their papers we have counts for
68 papers
ROAR: Reinforcing Original to Augmented Data Ratio Dynamics for Wav2Vec2.0 Based ASR
Vishwanath Pratap Singh, Federico Malato, Ville Hautamaki +2
While automatic speech recognition (ASR) greatly benefits from data augmentation, the augmentation recipes themselves tend to be heuristic. In this paper, we address one of the heu…
Revisiting and Improving Scoring Fusion for Spoofing-aware Speaker Verification Using Compositional Data Analysis
Xin Wang, Tomi Kinnunen, Kong Aik Lee +2
Fusing outputs from automatic speaker verification (ASV) and spoofing countermeasure (CM) is expected to make an integrated system robust to zero-effort imposters and synthesized s…
ChildAugment: Data Augmentation Methods for Zero-Resource Children's Speaker Verification
Vishwanath Pratap Singh, Md Sahidullah, Tomi Kinnunen
The accuracy of modern automatic speaker verification (ASV) systems, when trained exclusively on adult data, drops substantially when applied to children's speech. The scarcity of…
Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
Xuechen Liu, Md Sahidullah, Kong Aik Lee +1
It is now well-known that automatic speaker verification (ASV) systems can be spoofed using various types of adversaries. The usual approach to counteract ASV systems against such…
t-EER: Parameter-Free Tandem Evaluation of Countermeasures and Biometric Comparators
Tomi Kinnunen, Kong Aik Lee, Hemlata Tak +2
Presentation attack (spoofing) detection (PAD) typically operates alongside biometric verification to improve reliablity in the face of spoofing attacks. Even though the two sub-sy…
Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech
Vishwanath Pratap Singh, Md Sahidullah, Tomi Kinnunen
In this paper, we study the impact of the ageing on modern deep speaker embedding based automatic speaker verification (ASV) systems. We have selected two different datasets to exa…
Multi-Dataset Co-Training with Sharpness-Aware Optimization for Audio Anti-spoofing
Hye-jin Shim, Jee-weon Jung, Tomi Kinnunen
Audio anti-spoofing for automatic speaker verification aims to safeguard users' identities from spoofing attacks. Although state-of-the-art spoofing countermeasure(CM) models perfo…
Towards single integrated spoofing-aware speaker verification embeddings
Sung Hwan Mun, Hye-jin Shim, Hemlata Tak +12
This study aims to develop a single integrated spoofing-aware speaker verification (SASV) embeddings that satisfy two aspects. First, rejecting non-target speakers' input as well a…
How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning
Hye-jin Shim, Rosa González Hautamäki, Md Sahidullah +1
Shortcut learning, or `Clever Hans effect` refers to situations where a learning agent (e.g., deep neural networks) learns spurious correlations present in data, resulting in biase…
Speaker-Aware Anti-Spoofing
Xuechen Liu, Md Sahidullah, Kong Aik Lee +1
We address speaker-aware anti-spoofing, where prior knowledge of the target speaker is incorporated into a voice spoofing countermeasure (CM). In contrast to the frequently used sp…
Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification
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, p…
Learnable Frontends that do not Learn: Quantifying Sensitivity to Filterbank Initialisation
Mark Anderson, Tomi Kinnunen, Naomi Harte
While much of modern speech and audio processing relies on deep neural networks trained using fixed audio representations, recent studies suggest great potential in acoustic fronte…
I4U System Description for NIST SRE'20 CTS Challenge
Kong Aik Lee, Tomi Kinnunen, Daniele Colibro +23
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge. The I4U's submission was resu…
ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild
Xuechen Liu, Xin Wang, Md Sahidullah +8
Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typic…
An Initial study on Birdsong Re-synthesis Using Neural Vocoders
Rhythm Bhatia, Tomi H. Kinnunen
Modern speech synthesis uses neural vocoders to model raw waveform samples directly. This increased versatility has expanded the scope of vocoders from speech to other domains, suc…
GAN-Aimbots: Using Machine Learning for Cheating in First Person Shooters
Anssi Kanervisto, Tomi Kinnunen, Ville Hautamäki
Playing games with cheaters is not fun, and in a multi-billion-dollar video game industry with hundreds of millions of players, game developers aim to improve the security and, con…
Gamified Speaker Comparison by Listening
Sandip Ghimire, Tomi Kinnunen, Rosa Gonzalez Hautamäki
We address speaker comparison by listening in a game-like environment, hypothesized to make the task more motivating for naive listeners. We present the same 30 trials selected wit…
Baselines and Protocols for Household Speaker Recognition
Alexey Sholokhov, Xuechen Liu, Md Sahidullah +1
Speaker recognition on household devices, such as smart speakers, features several challenges: (i) robustness across a vast number of heterogeneous domains (households), (ii) short…
Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation
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 counterme…
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
Hye-jin Shim, Hemlata Tak, Xuechen Liu +12
Deep learning has brought impressive progress in the study of both automatic speaker verification (ASV) and spoofing countermeasures (CM). Although solutions are mutually dependent…
Improving speaker de-identification with functional data analysis of f0 trajectories
Lauri Tavi, Tomi Kinnunen, Rosa González Hautamäki
Due to a constantly increasing amount of speech data that is stored in different types of databases, voice privacy has become a major concern. To respond to such concern, speech re…
SASV 2022: The First Spoofing-Aware Speaker Verification Challenge
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +6
The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scen…
SASV Challenge 2022: A Spoofing Aware Speaker Verification Challenge Evaluation Plan
Jee-weon Jung, Hemlata Tak, Hye-jin Shim +7
ASV (automatic speaker verification) systems are intrinsically required to reject both non-target (e.g., voice uttered by different speaker) and spoofed (e.g., synthesised or conve…
Learnable Nonlinear Compression for Robust Speaker Verification
Xuechen Liu, Md Sahidullah, Tomi Kinnunen
In this study, we focus on nonlinear compression methods in spectral features for speaker verification based on deep neural network. We consider different kinds of channel-dependen…