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Found 58 papers, 7 papers with code
Self-Tuning Spectral Clustering for Speaker Diarization
Spectral clustering has proven effective in grouping speech representations for speaker diarization tasks, although post-processing the affinity matrix remains difficult due to the need for careful tuning before constructing the Laplacian.
TCG CREST System Description for the Second DISPLACE Challenge
In this report, we describe the speaker diarization (SD) and language diarization (LD) systems developed by our team for the Second DISPLACE Challenge, 2024.
Graph Neural Networks for Parkinsons Disease Detection
Additionally, not all speech segments from PD patients exhibit clear dysarthric symptoms, introducing label noise that can negatively affect the performance and generalizability of current approaches.
ASVspoof 5: Crowdsourced Speech Data, Deepfakes, and Adversarial Attacks at Scale
ASVspoof 5 is the fifth edition in a series of challenges that promote the study of speech spoofing and deepfake attacks, and the design of detection solutions.
Beyond Silence: Bias Analysis through Loss and Asymmetric Approach in Audio Anti-Spoofing
Our investigations highlight the significant differences in training dynamics between the two classes, emphasizing the need for future research to focus on robust modeling of the bonafide class.
Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization
Clustering speaker embeddings is crucial in speaker diarization but hasn't received as much focus as other components.
Exploring Green AI for Audio Deepfake Detection
In contrast to existing methods that fine-tune SSL models and employ additional deep neural networks for downstream tasks, we exploit classical machine learning algorithms such as logistic regression and shallow neural networks using the SSL embeddings extracted using the pre-trained model.
Ranked #2 on
Voice Anti-spoofing
on ASVspoof 2019 - LA
ChildAugment: Data Augmentation Methods for Zero-Resource Children's Speaker Verification
One promising approach is to align vocal-tract parameters between adults and children through children-specific data augmentation, referred here to as ChildAugment.
Generalizing Speaker Verification for Spoof Awareness in the Embedding Space
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.
Speaker Verification Across Ages: Investigating Deep Speaker Embedding Sensitivity to Age Mismatch in Enrollment and Test Speech
The first dataset, used for addressing short-term ageing (up to 10 years time difference between enrollment and test) under uncontrolled conditions, is VoxCeleb.
Stuttering Detection Using Speaker Representations and Self-supervised Contextual Embeddings
The adoption of advanced deep learning architectures in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets.
How to Construct Perfect and Worse-than-Coin-Flip Spoofing Countermeasures: A Word of Warning on Shortcut Learning
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 biased models.
Towards single integrated spoofing-aware speaker verification embeddings
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.
Distilling Multi-Level X-vector Knowledge for Small-footprint Speaker Verification
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.
Advancing Stuttering Detection via Data Augmentation, Class-Balanced Loss and Multi-Contextual Deep Learning
In addition, we propose a multi-contextual (MC) StutterNet, which exploits different contexts of the stuttered speech, resulting in an overall improvement of 4. 48% in F 1 over the single context based MB StutterNet.
Cross-Corpora Spoken Language Identification with Domain Diversification and Generalization
Our experiments demonstrate that the proposed domain diversification is more promising over commonly used simple augmentation methods.
Modulation spectral features for speech emotion recognition using deep neural networks
This work explores the use of constant-Q transform based modulation spectral features (CQT-MSF) for speech emotion recognition (SER).
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field.
Analysis of constant-Q filterbank based representations for speech emotion recognition
This work analyzes the constant-Q filterbank-based time-frequency representations for speech emotion recognition (SER).
I4U System Description for NIST SRE'20 CTS Challenge
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
End-to-End and Self-Supervised Learning for ComParE 2022 Stuttering Sub-Challenge
In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifically the stuttering sub-challenge.
Baselines and Protocols for Household Speaker Recognition
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).
Robust Stuttering Detection via Multi-task and Adversarial Learning
By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS).
Introducing ECAPA-TDNN and Wav2Vec2.0 Embeddings to Stuttering Detection
The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets.
Spoofing-Aware Speaker Verification with Unsupervised Domain Adaptation
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.
Learnable Nonlinear Compression for Robust Speaker Verification
We consider different kinds of channel-dependent (CD) nonlinear compression methods optimized in a data-driven manner.
Optimizing Multi-Taper Features for Deep Speaker Verification
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).
Parameterized Channel Normalization for Far-field Deep Speaker Verification
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.
Optimized Power Normalized Cepstral Coefficients towards Robust Deep Speaker Verification
After their introduction to robust speech recognition, power normalized cepstral coefficient (PNCC) features were successfully adopted to other tasks, including speaker verification.
ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
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.
ASVspoof 2021: Automatic Speaker Verification Spoofing and Countermeasures Challenge Evaluation Plan
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.
Machine Learning for Stuttering Identification: Review, Challenges and Future Directions
Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds.
Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
Whether it be for results summarization, or the analysis of classifier fusion, some means to compare different classifiers can often provide illuminating insight into their behaviour, (dis)similarity or complementarity.
Utterance partitioning for speaker recognition: an experimental review and analysis with new findings under GMM-SVM framework
This work presents a detailed experimental review and analysis of the GMM-SVM based speaker recognition system in presence of duration variability.
StutterNet: Stuttering Detection Using Time Delay Neural Network
Compared to the existing work, which depends on the ASR module, our method relies solely on the acoustic signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Deep scattering network for speech emotion recognition
We also investigate layer-wise scattering coefficients to analyse the importance of time shift and deformation stable scalogram and modulation spectrum coefficients for SER.
Cross-Corpora Language Recognition: A Preliminary Investigation with Indian Languages
In this paper, we conduct one of the very first studies for cross-corpora performance evaluation in the spoken language identification (LID) problem.
Data Quality as Predictor of Voice Anti-Spoofing Generalization
Voice anti-spoofing aims at classifying a given utterance either as a bonafide human sample, or a spoofing attack (e. g. synthetic or replayed sample).
Learnable MFCCs for Speaker Verification
We propose a learnable mel-frequency cepstral coefficient (MFCC) frontend architecture for deep neural network (DNN) based automatic speaker verification.
ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech
The ASVspoof initiative was conceived to spearhead research in anti-spoofing for automatic speaker verification (ASV).
Non-linear frequency warping using constant-Q transformation for speech emotion recognition
In this work, we explore the constant-Q transform (CQT) for speech emotion recognition (SER).
Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification
In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV).
Domain-Dependent Speaker Diarization for the Third DIHARD Challenge
This report presents the system developed by the ABSP Laboratory team for the third DIHARD speech diarization challenge.
A Comparative Re-Assessment of Feature Extractors for Deep Speaker Embeddings
Modern automatic speaker verification relies largely on deep neural networks (DNNs) trained on mel-frequency cepstral coefficient (MFCC) features.
UIAI System for Short-Duration Speaker Verification Challenge 2020
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.
Optimization of data-driven filterbank for automatic speaker verification
Then, we propose a new method for computing the filter frequency responses by using principal component analysis (PCA).
Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals
Recent years have seen growing efforts to develop spoofing countermeasures (CMs) to protect automatic speaker verification (ASV) systems from being deceived by manipulated or artificial inputs.
Evaluating Voice Conversion-based Privacy Protection against Informed Attackers
In this paper, we investigate anonymization methods based on voice conversion.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The Speed Submission to DIHARD II: Contributions & Lessons Learned
This paper describes the speaker diarization systems developed for the Second DIHARD Speech Diarization Challenge (DIHARD II) by the Speed team.
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques.
Voice Mimicry Attacks Assisted by Automatic Speaker Verification
Our goal is to gain insights how well similarity rankings transfer from the attacker's ASV system to the attacked ASV system, whether the attackers are able to improve their attacks by mimicking, and how the properties of the voices of attackers change due to mimicking.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Quality Measures for Speaker Verification with Short Utterances
We have used the proposed quality measures as side information for combining ASV systems based on Gaussian mixture model-universal background model (GMM-UBM) and i-vector.
Introduction to Voice Presentation Attack Detection and Recent Advances
Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV).
Novel Quality Metric for Duration Variability Compensation in Speaker Verification using i-Vectors
In experiments with the NIST SRE 2008 corpus, We have shown that inclusion of proposed quality metric exhibits considerable improvement in speaker verification performance.
Can We Use Speaker Recognition Technology to Attack Itself? Enhancing Mimicry Attacks Using Automatic Target Speaker Selection
We consider technology-assisted mimicry attacks in the context of automatic speaker verification (ASV).
t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification
The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric.
Robustness of Voice Conversion Techniques Under Mismatched Conditions
Most of the existing studies on voice conversion (VC) are conducted in acoustically matched conditions between source and target signal.
