Papers with ADD
Heterogeneity over Homogeneity: Investigating Multilingual Speech Pre-Trained Models for Detecting Audio Deepfake (2024.findings-naacl)
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| Challenge: | a recent study has focused on audio deepfake detection (ADD) due to its ability to impersonate and share false, often malicious information. |
| Approach: | They propose to use multilingual speech Pre-Trained models for Audio deepfake detection (ADD) they propose to combine models with existing models to achieve better ADD detection . |
| Outcome: | The proposed models gain knowledge about diverse pitches, accents, and tones, during theirpre-training phase and are more robust to variations. |
Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)
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| Challenge: | Existing audio deepfake detection datasets are outdated and lack generalization capabilities. |
| Approach: | They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models. |
| Outcome: | The proposed models achieve 4.1% and 6.5% error rates in the cross-domain ADD dataset generated by five advanced zero-shot TTS models. |
EditNTS: An Neural Programmer-Interpreter Model for Sentence Simplification through Explicit Editing (P19-1)
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| Challenge: | Current sentence simplification systems are variants of sequence-to-sequence models adopted from machine translation. |
| Approach: | They propose a sentence simplification model that learns explicit edit operations via a neural programmer-interpreter approach. |
| Outcome: | The proposed model outperforms state-of-the-art models on three benchmark text simplification corpora in terms of SARI (+0.95 WikiLarge, +1.89 WikiSmall, -1.41 Newsela) |
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning (2026.acl-long)
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Sikuan Yan, Xiufeng Yang, Zuchao Huang, Ercong Nie, Zifeng Ding, Zonggen Li, Xiaowen Ma, Jinhe Bi, Kristian Kersting, Jeff Z. Pan, Hinrich Schuetze, Volker Tresp, Yunpu Ma
| Challenge: | Large Language Models (LLMs) are stateless and limited by a finite context window, preventing them from maintaining knowledge across long conversations or evolving tasks. |
| Approach: | They propose a reinforcement learning framework that empowers LLMs to actively manage external memory through two specialized agents. |
| Outcome: | The proposed framework outperforms baselines and benchmarks across diverse question types, three benchmarks, and multiple model scales. |
Inside Out: Evolving User-Centric Core Memory Trees for Long-Term Personalized Dialogue Systems (2026.acl-long)
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| Challenge: | Existing personalized dialogue systems struggle to reconcile unbounded interactions with finite context constraints. |
| Approach: | They propose a framework that utilizes a globally maintained PersonaTree as the carrier of long-term user profiling. |
| Outcome: | The proposed framework outperforms existing systems in suppressing contextual noise and persona inconsistency. |
IndicSynth: A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages (2025.acl-long)
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| Challenge: | Recent advances in synthetic speech generation technology have enabled the generation of high-quality synthetic (fake) speech that emulates human voices. |
| Approach: | They propose a dataset that contains 4,000 hours of synthetic speech from 989 target speakers for 12 low-resourced Indian languages. |
| Outcome: | The proposed dataset contains 4,000 hours of synthetic speech from 989 target speakers, including 456 females and 533 males for 12 low-resourced Indian languages. |
XLSR-MamBo: Scaling the Hybrid Mamba-Attention Backbone for Audio Deepfake Detection (2026.findings-acl)
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| Challenge: | Advanced speech synthesis technologies have enabled highly realistic speech generation, posing security risks that motivate research into audio deepfake detection (ADD). |
| Approach: | They propose a modular framework that integrates an XLSR front-end with synergistic Mamba-Attention backbones to capture artifacts in spoofed speech signals. |
| Outcome: | The proposed framework achieves competitive performance on the ASVspoof 2021 LA, DF, and In-the-Wild benchmarks compared to other state-of-the art systems. |
BRSpeech-DF: A Deep Fake Synthetic Speech Dataset for Portuguese Zero-Shot TTS (2025.emnlp-main)
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Alexandre Costa Ferro Filho, Rafaello Virgilli, Lucas Alcantara Souza, F S de Oliveira, Marcelo Henrique Lopes Ferreira, Daniel Tunnermann, Gustavo Dos Reis Oliveira, Anderson Da Silva Soares, Arlindo Rodrigues Galvão Filho
| Challenge: | ADD detection is a key area of research for low-resource languages like Portuguese, which lacks high-quality datasets. |
| Approach: | They propose to provide the first publicly available ADD dataset for Portuguese, encompassing both Brazilian and European variants. |
| Outcome: | The proposed dataset contains over 458,000 utterances, including a smaller portion of real speech from 62 speakers and a large collection of synthetic samples generated using multiple zero-shot text-to-speech (TTS) models, each conditioned on the original speaker’s voice. |