# PDAF: A Phonetic Debiasing Attention Framework For Speaker Verification

arXiv: https://arxiv.org/abs/2409.05799 · HTML: https://mlsanity.com/paper/2409.05799/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [Self-attention encoding and pooling for speaker recognition](https://mlsanity.com/paper/2008.01077/) (2020)
- [Phone-to-audio alignment without text: A Semi-supervised Approach](https://mlsanity.com/paper/2110.03876/) (2021)
- [Speaker Embedding Extraction with Phonetic Information](https://mlsanity.com/paper/1804.04862/) (2018)
- [Frequency and Multi-Scale Selective Kernel Attention for Speaker Verification](https://mlsanity.com/paper/2204.01005/) (2022)
- [Incorporating Uncertainty from Speaker Embedding Estimation to Speaker Verification](https://mlsanity.com/paper/2302.11763/) (2023)
- [How phonemes contribute to deep speaker models?](https://mlsanity.com/paper/2402.02730/) (2024)
- [Phonetic-attention scoring for deep speaker features in speaker verification](https://mlsanity.com/paper/1811.03255/) (2018)

## Built on by (most cited first, 3 of 3)

- [CoLMbo: Speaker Language Model for Descriptive Profiling](https://mlsanity.com/paper/2506.09375/) (2025)
- [DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Trained Speech Foundational Model](https://mlsanity.com/paper/2510.17662/) (2025)
- [What and When to Learn: CURriculum Ranking Loss for Large-Scale Speaker Verification](https://mlsanity.com/paper/2603.24432/) (2026)

## Abstract (arXiv)

Speaker verification systems are crucial for authenticating identity through voice. Traditionally, these systems focus on comparing feature vectors, overlooking the speech's content. However, this paper challenges this by highlighting the importance of phonetic dominance, a measure of the frequency or duration of phonemes, as a crucial cue in speaker verification. A novel Phoneme Debiasing Attention Framework (PDAF) is introduced, integrating with existing attention frameworks to mitigate biases caused by phonetic dominance. PDAF adjusts the weighting for each phoneme and influences feature extraction, allowing for a more nuanced analysis of speech. This approach paves the way for more accurate and reliable identity authentication through voice. Furthermore, by employing various weighting strategies, we evaluate the influence of phonetic features on the efficacy of the speaker verification system.