ArFake: A Multi-Dialect Benchmark and Baselines for Arabic Spoof-Speech Detection

• URL: http://arxiv.org/abs/2509.22808v1
• Date: Fri, 26 Sep 2025 18:11:20 GMT
• Title: ArFake: A Multi-Dialect Benchmark and Baselines for Arabic Spoof-Speech Detection
• Authors: Mohamed Maged, Alhassan Ehab, Ali Mekky, Besher Hassan, Shady Shehata,
• Abstract summary: We introduce the first multi-dialect Arabic spoofed speech dataset.<n>Our results demonstrate that FishSpeech outperforms other TTS models in Arabic voice cloning on the Casablanca corpus.
• Score: 2.5962590697722447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rise of generative text-to-speech models, distinguishing between real and synthetic speech has become challenging, especially for Arabic that have received limited research attention. Most spoof detection efforts have focused on English, leaving a significant gap for Arabic and its many dialects. In this work, we introduce the first multi-dialect Arabic spoofed speech dataset. To evaluate the difficulty of the synthesized audio from each model and determine which produces the most challenging samples, we aimed to guide the construction of our final dataset either by merging audios from multiple models or by selecting the best-performing model, we conducted an evaluation pipeline that included training classifiers using two approaches: modern embedding-based methods combined with classifier heads; classical machine learning algorithms applied to MFCC features; and the RawNet2 architecture. The pipeline further incorporated the calculation of Mean Opinion Score based on human ratings, as well as processing both original and synthesized datasets through an Automatic Speech Recognition model to measure the Word Error Rate. Our results demonstrate that FishSpeech outperforms other TTS models in Arabic voice cloning on the Casablanca corpus, producing more realistic and challenging synthetic speech samples. However, relying on a single TTS for dataset creation may limit generalizability.

Related papers

• LLM-to-Speech: A Synthetic Data Pipeline for Training Dialectal Text-to-Speech Models [0.0]
  We introduce NileTTS: 38 hours of transcribed speech from two speakers across diverse domains.<n>Large language models (LLM) generate Egyptian Arabic content, which is converted to natural speech using audio synthesis tools.<n>We fine-tune XTTS v2, a state-of-the-art multilingual TTS model, on our dataset and evaluate against the baseline model trained on other Arabic dialects.
  arXiv  Detail & Related papers  (2026-02-17T15:58:27Z)
• UALM: Unified Audio Language Model for Understanding, Generation and Reasoning [124.19449187588832]
  Unified Audio Language Model (UALM) aims to unify audio understanding, text-to-audio generation, and multimodal reasoning in a single model.<n>We first present UALM-Gen, a text-to-audio language model that directly predicts audio tokens and is comparable to state-of-the-art diffusion-based models.<n>We present UALM-Reason, a multimodal reasoning model that utilizes both text and audio in the intermediate thinking steps to facilitate complex generation tasks.
  arXiv  Detail & Related papers  (2025-10-13T22:55:01Z)
• AHELM: A Holistic Evaluation of Audio-Language Models [78.20477815156484]
  multimodal audio-language models (ALMs) take interleaved audio and text as input and output text.<n>AHELM is a benchmark that aggregates various datasets -- including 2 new synthetic audio-text datasets called PARADE and CoRe-Bench.<n>We also standardize the prompts, inference parameters, and evaluation metrics to ensure equitable comparisons across models.
  arXiv  Detail & Related papers  (2025-08-29T07:40:39Z)
• Hybrid Deep Learning and Signal Processing for Arabic Dialect Recognition in Low-Resource Settings [0.0]
  Arabic dialect recognition presents a significant challenge due to the linguistic diversity of Arabic and the scarcity of large annotated datasets.<n>This research investigates hybrid modeling strategies that integrate classical signal processing techniques with deep learning architectures.
  arXiv  Detail & Related papers  (2025-06-26T15:36:25Z)
• Where are we in audio deepfake detection? A systematic analysis over generative and detection models [59.09338266364506]
  SONAR is a synthetic AI-Audio Detection Framework and Benchmark.<n>It provides a comprehensive evaluation for distinguishing cutting-edge AI-synthesized auditory content.<n>It is the first framework to uniformly benchmark AI-audio detection across both traditional and foundation model-based detection systems.
  arXiv  Detail & Related papers  (2024-10-06T01:03:42Z)
• Towards Zero-Shot Text-To-Speech for Arabic Dialects [16.10882912169842]
  Zero-shot multi-speaker text-to-speech (ZS-TTS) systems have advanced for English, however, it still lags behind due to insufficient resources. We address this gap for Arabic by first adapting an existing dataset to suit the needs of speech synthesis. We employ a set of Arabic dialect identification models to explore the impact of pre-defined dialect labels on improving the ZS-TTS model in a multi-dialect setting.
  arXiv  Detail & Related papers  (2024-06-24T15:58:15Z)
• Mispronunciation detection using self-supervised speech representations [10.010024759851142]
  We study the use of SSL models for the task of mispronunciation detection for second language learners. We compare two downstream approaches: 1) training the model for phone recognition using native English data, and 2) training a model directly for the target task using non-native English data.
  arXiv  Detail & Related papers  (2023-07-30T21:20:58Z)
• Towards Language Modelling in the Speech Domain Using Sub-word Linguistic Units [56.52704348773307]
  We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes. With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech. We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
  arXiv  Detail & Related papers  (2021-10-31T22:48:30Z)
• LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech [67.88748572167309]
  We present LDNet, a unified framework for mean opinion score (MOS) prediction. We propose two inference methods that provide more stable results and efficient computation.
  arXiv  Detail & Related papers  (2021-10-18T08:52:31Z)
• Direct speech-to-speech translation with discrete units [64.19830539866072]
  We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We propose to predict the self-supervised discrete representations learned from an unlabeled speech corpus instead. When target text transcripts are available, we design a multitask learning framework with joint speech and text training that enables the model to generate dual mode output (speech and text) simultaneously in the same inference pass.
  arXiv  Detail & Related papers  (2021-07-12T17:40:43Z)

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.