# NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation

arXiv: https://arxiv.org/abs/2509.03829 · HTML: https://mlsanity.com/paper/2509.03829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 24 references that are in the map.
- First posted on arXiv 4 Sep 2025.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis](https://mlsanity.com/paper/2010.05646/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networks](https://mlsanity.com/paper/2110.01200/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [FreeVC: Towards High-Quality Text-Free One-Shot Voice Conversion](https://mlsanity.com/paper/2210.15418/) (2022)
- [The PartialSpoof Database and Countermeasures for the Detection of Short Fake Speech Segments Embedded in an Utterance](https://mlsanity.com/paper/2204.05177/) (2022)
- [AGAIN-VC: A One-shot Voice Conversion using Activation Guidance and Adaptive Instance Normalization](https://mlsanity.com/paper/2011.00316/) (2020)
- [Half-Truth: A Partially Fake Audio Detection Dataset](https://mlsanity.com/paper/2104.03617/) (2021)
- [End-to-end Named Entity Recognition from English Speech](https://mlsanity.com/paper/2005.11184/) (2020)
- [Grad-StyleSpeech: Any-speaker Adaptive Text-to-Speech Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09383/) (2022)
- [SpeechSplit 2.0: Unsupervised speech disentanglement for voice conversion Without tuning autoencoder Bottlenecks](https://mlsanity.com/paper/2203.14156/) (2022)
- [An Initial Investigation for Detecting Partially Spoofed Audio](https://mlsanity.com/paper/2104.02518/) (2021)
- [AVQVC: One-shot Voice Conversion by Vector Quantization with applying contrastive learning](https://mlsanity.com/paper/2202.10020/) (2022)
- [Multi-Task Learning in Utterance-Level and Segmental-Level Spoof Detection](https://mlsanity.com/paper/2107.14132/) (2021)
- [Waveform Boundary Detection for Partially Spoofed Audio](https://mlsanity.com/paper/2211.00226/) (2022)
- [An Efficient Temporary Deepfake Location Approach Based Embeddings for Partially Spoofed Audio Detection](https://mlsanity.com/paper/2309.03036/) (2023)
- [Enhancing Partially Spoofed Audio Localization with Boundary-aware Attention Mechanism](https://mlsanity.com/paper/2407.21611/) (2024)
- [Enhancing Expressive Voice Conversion with Discrete Pitch-Conditioned Flow Matching Model](https://mlsanity.com/paper/2502.05471/) (2025)
- [Stable-TTS: Stable Speaker-Adaptive Text-to-Speech Synthesis via Prosody Prompting](https://mlsanity.com/paper/2412.20155/) (2024)
- [DiffCSS: Diverse and Expressive Conversational Speech Synthesis with Diffusion Models](https://mlsanity.com/paper/2502.19924/) (2025)

## Built on by (most cited first, 1 of 1)

- [TRACE: Training-Free Partial Audio Deepfake Detection via Embedding Trajectory Analysis of Speech Foundation Models](https://mlsanity.com/paper/2604.01083/) (2026)

## Abstract (arXiv)

Different from traditional sentence-level audio deepfake detection (ADD), partial audio deepfake detection (PADD) requires frame-level positioning of the location of fake speech. While some progress has been made in this area, leveraging semantic information from audio, especially named entities, remains an underexplored aspect. To this end, we propose NE-PADD, a novel method for Partial Audio Deepfake Detection (PADD) that leverages named entity knowledge through two parallel branches: Speech Name Entity Recognition (SpeechNER) and PADD. The approach incorporates two attention aggregation mechanisms: Attention Fusion (AF) for combining attention weights and Attention Transfer (AT) for guiding PADD with named entity semantics using an auxiliary loss. Built on the PartialSpoof-NER dataset, experiments show our method outperforms existing baselines, proving the effectiveness of integrating named entity knowledge in PADD. The code is available at https://github.com/AI-S2-Lab/NE-PADD.