A New Transformer-Based Approach for Audio-Based Kinship Verification and a New Uncontrolled Mandarin Kinship Speech Dataset
- URL: http://arxiv.org/abs/2609.14145v1
- Date: Sat, 12 Sep 2026 20:49:53 GMT
- Title: A New Transformer-Based Approach for Audio-Based Kinship Verification and a New Uncontrolled Mandarin Kinship Speech Dataset
- Abstract summary: We propose CONVTRAP-TN, a new architecture for audio-based kinship verification.<n>We also collect a custom speech dataset, ARKIN, which accurately reflects everyday recording conditions.
- Score: 2.5265731030761875
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Kinship verification is a task involving determining whether two individuals share a first-order kin relation. To tackle this task, we propose CONVTRAP-TN, a new architecture for audio-based kinship verification, and conduct an ablation study on the proposed model. To the best of our knowledge, we are the first to apply the successful transformer architecture to the task of audio-based kinship verification. Furthermore, we also collect a custom speech dataset, ARKIN, which accurately reflects everyday recording conditions. We do this because only a few speech datasets with kinship labels currently exist, all of which either source extremely noisy in-the-wild data from the internet, or instruct speakers to record in specific environments. These settings fail to reflect real-world scenarios where users record on personal devices under unrestrained conditions. Additionally, we perform a series of preliminary baseline experiments on the collected dataset, including speaker verification and recognition, speech recognition, age estimation, and kinship verification, as well as cross-dataset kinship verification experiments to show that existing methods are not robust across datasets.
Related papers
- CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking [85.68235482145091]
Large-scale speech datasets have become valuable intellectual property.<n>We propose a novel dataset ownership verification method.<n>Our approach introduces a clustering-based backdoor watermark (CBW)<n>We conduct extensive experiments on benchmark datasets, verifying the effectiveness and robustness of our method against potential adaptive attacks.
arXiv Detail & Related papers (2025-03-02T02:02:57Z) - Audio-based Kinship Verification Using Age Domain Conversion [39.4890403254022]
Key challenge in the task arises from differences in age across samples from different individuals.
We utilise the optimised CycleGAN-VC3 network to perform age-audio conversion to generate the in-domain audio.
The generated audio dataset is employed to extract a range of features, which are then fed into a metric learning architecture to verify kinship.
arXiv Detail & Related papers (2024-10-14T22:08:57Z) - Real Acoustic Fields: An Audio-Visual Room Acoustics Dataset and Benchmark [65.79402756995084]
Real Acoustic Fields (RAF) is a new dataset that captures real acoustic room data from multiple modalities.
RAF is the first dataset to provide densely captured room acoustic data.
arXiv Detail & Related papers (2024-03-27T17:59:56Z) - REWIND Dataset: Privacy-preserving Speaking Status Segmentation from
Multimodal Body Movement Signals in the Wild [14.5263556841263]
We present the first publicly available multimodal dataset with high-quality individual speech recordings of 33 subjects in a professional networking event.
In all cases we predict a 20Hz binary speaking status signal extracted from the audio, a time resolution not available in previous datasets.
arXiv Detail & Related papers (2024-03-02T15:14:58Z) - Do You Remember? Overcoming Catastrophic Forgetting for Fake Audio
Detection [54.20974251478516]
We propose a continual learning algorithm for fake audio detection to overcome catastrophic forgetting.
When fine-tuning a detection network, our approach adaptively computes the direction of weight modification according to the ratio of genuine utterances and fake utterances.
Our method can easily be generalized to related fields, like speech emotion recognition.
arXiv Detail & Related papers (2023-08-07T05:05:49Z) - Self-Supervised Visual Acoustic Matching [63.492168778869726]
Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment.
We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio.
Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric.
arXiv Detail & Related papers (2023-07-27T17:59:59Z) - ASiT: Local-Global Audio Spectrogram vIsion Transformer for Event
Classification [42.95038619688867]
ASiT is a novel self-supervised learning framework that captures local and global contextual information by employing group masked model learning and self-distillation.
We evaluate our pretrained models on both audio and speech classification tasks, including audio event classification, keyword spotting, and speaker identification.
arXiv Detail & Related papers (2022-11-23T18:21:09Z) - Unsupervised Domain Adaptation for Acoustic Scene Classification Using
Band-Wise Statistics Matching [69.24460241328521]
Machine learning algorithms can be negatively affected by mismatches between training (source) and test (target) data distributions.
We propose an unsupervised domain adaptation method that consists of aligning the first- and second-order sample statistics of each frequency band of target-domain acoustic scenes to the ones of the source-domain training dataset.
We show that the proposed method outperforms the state-of-the-art unsupervised methods found in the literature in terms of both source- and target-domain classification accuracy.
arXiv Detail & Related papers (2020-04-30T23:56:05Z) - Deep Speaker Embeddings for Far-Field Speaker Recognition on Short
Utterances [53.063441357826484]
Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions.
Speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks.
This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances.
arXiv Detail & Related papers (2020-02-14T13:34:33Z) - Robust Speaker Recognition Using Speech Enhancement And Attention Model [37.33388614967888]
Instead of individually processing speech enhancement and speaker recognition, the two modules are integrated into one framework by a joint optimisation using deep neural networks.
To increase robustness against noise, a multi-stage attention mechanism is employed to highlight the speaker related features learned from context information in time and frequency domain.
The obtained results show that the proposed approach using speech enhancement and multi-stage attention models outperforms two strong baselines not using them in most acoustic conditions in our experiments.
arXiv Detail & Related papers (2020-01-14T20:03:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.