Search Results for author:
Found 19 papers, 11 papers with code
HM-Conformer: A Conformer-based audio deepfake detection system with hierarchical pooling and multi-level classification token aggregation methods
Audio deepfake detection (ADD) is the task of detecting spoofing attacks generated by text-to-speech or voice conversion systems.
Diff-SV: A Unified Hierarchical Framework for Noise-Robust Speaker Verification Using Score-Based Diffusion Probabilistic Models
Diff-SV unifies a DPM-based speech enhancement system with a speaker embedding extractor, and yields a discriminative and noise-tolerable speaker representation through a hierarchical structure.
PAS: Partial Additive Speech Data Augmentation Method for Noise Robust Speaker Verification
In this paper, we propose a new additive noise method, partial additive speech (PAS), which aims to train SV systems to be less affected by noisy environments.
One-Step Knowledge Distillation and Fine-Tuning in Using Large Pre-Trained Self-Supervised Learning Models for Speaker Verification
This paper suggests One-Step Knowledge Distillation and Fine-Tuning (OS-KDFT), which incorporates KD and fine-tuning (FT).
Integrated Parameter-Efficient Tuning for General-Purpose Audio Models
To overcome these limitations, the present study explores and applies efficient transfer learning methods in the audio domain.
Convolution channel separation and frequency sub-bands aggregation for music genre classification
In music, short-term features such as pitch and tempo constitute long-term semantic features such as melody and narrative.
Two Methods for Spoofing-Aware Speaker Verification: Multi-Layer Perceptron Score Fusion Model and Integrated Embedding Projector
The use of deep neural networks (DNN) has dramatically elevated the performance of automatic speaker verification (ASV) over the last decade.
Extended U-Net for Speaker Verification in Noisy Environments
Background noise is a well-known factor that deteriorates the accuracy and reliability of speaker verification (SV) systems by blurring speech intelligibility.
RawNeXt: Speaker verification system for variable-duration utterances with deep layer aggregation and extended dynamic scaling policies
Despite achieving satisfactory performance in speaker verification using deep neural networks, variable-duration utterances remain a challenge that threatens the robustness of systems.
Attentive max feature map and joint training for acoustic scene classification
Furthermore, adopting the proposed attentive max feature map, our team placed fourth in the recent DCASE 2021 challenge.
Learning Metrics from Mean Teacher: A Supervised Learning Method for Improving the Generalization of Speaker Verification System
By learning the reliable intermediate representation of the mean teacher network, we expect that the proposed method can explore more discriminatory embedding spaces and improve the generalization performance of the speaker verification system.
DCASENET: A joint pre-trained deep neural network for detecting and classifying acoustic scenes and events
Single task deep neural networks that perform a target task among diverse cross-related tasks in the acoustic scene and event literature are being developed.
Capturing scattered discriminative information using a deep architecture in acoustic scene classification
Various experiments are conducted using the detection and classification of acoustic scenes and events 2020 task1-a dataset to validate the proposed methods.
Integrated Replay Spoofing-aware Text-independent Speaker Verification
In this paper, we propose two approaches for building an integrated system of speaker verification and presentation attack detection: an end-to-end monolithic approach and a back-end modular approach.
Segment Aggregation for short utterances speaker verification using raw waveforms
The proposed method segments an input utterance into several short utterances and then aggregates the segment embeddings extracted from the segmented inputs to compose a speaker embedding.
Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms.
RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verification
In this study, we explore end-to-end deep neural networks that input raw waveforms to improve various aspects: front-end speaker embedding extraction including model architecture, pre-training scheme, additional objective functions, and back-end classification.
Teaching Syntax by Adversarial Distraction
Existing entailment datasets mainly pose problems which can be answered without attention to grammar or word order.
Kapre: On-GPU Audio Preprocessing Layers for a Quick Implementation of Deep Neural Network Models with Keras
We introduce Kapre, Keras layers for audio and music signal preprocessing.
