IndicFake Meets SAFARI-LLM: Unifying Semantic and Acoustic Intelligence for Multilingual Deepfake Detection
Authors: Rishabh Ranjan
Mayank Vatsa
Richa Singh
Venue: Accepted by TMLR ()

Abstract: Audio deepfakes pose a growing threat, particularly in linguistically diverse and low-resource settings where existing detection methods often struggle. This work introduces two transformative contributions to address these challenges. First, we present \textbf{IndicFake}, a pioneering audio deepfake dataset with over 4.2 million samples (7,350 hours) spanning English and 17 Indian languages across Indo-European, Dravidian, and Sino-Tibetan families. With minimal overlap (Jaccard similarity: 0.00--0.06) with existing datasets, IndicFake offers an unparalleled benchmark for multilingual deepfake detection. Second, we propose \textbf{SAFARI-LLM} (Semantic Acoustic Feature Adaptive Router with Integrated LLM), a novel framework that integrates Whisper’s semantic embeddings and m-HuBERT’s acoustic features through an adaptive Audio Feature Unification Module (AFUM). Enhanced by LoRA-fine-tuned LLaMA-7B, SAFARI-LLM achieves unmatched cross-lingual and cross-family generalization. Evaluations across IndicFake, DECRO, and WaveFake datasets demonstrate its superiority, outperforming 14 state-of-the-art models with standout accuracies of 94.21\% (English-to-Japanese transfer on WaveFake) and 84.48\% (English-to-Chinese transfer on DECRO), alongside robust performance across diverse linguistic contexts. These advancements establish a new standard for reliable, scalable audio deepfake detection. Code and resources are publicly available at: https://anonymousillusion.github.io/indicfake/.

Published in Transactions on Machine Learning Research (11/2025)
IndicFake Meets SAFARI-LLM: Unifying Semantic and Acoustic Intelligence for Multilingual Deepfake Detection
Rishabh Ranjan
vanjon.4@iitj. ac.
Deportment of Computer Science Indian Institute of Technology Jodhpur
Mayank Vatsa
meatsa@iitjj.cmm ac..i
Indian Institute of Technology Jodkpur
Richa Singh
richa@ittj.ou oc. oc.in
Indian Insttuut of Technology Jodhpur
Reviewed OpenReview: https:// oppp:e///penrevveuu net/ forum? id= SPPPYVVVUU
Abstract
Audio deepfakes pose growing threat, particularly in linguistically diverse and low-resource settings where existing detection methods often struggle. This work introduces two transfor- mative contributions to address these challenges. First, we present IndicFake, a pioneering audio deepfake dataset with over 4.2 million samples (7,350 hours) spanning English and 17 Indian languages across Endo-European, Dravidian, and Sino-Tibetan families. With minimal overlap (Jaccard similarity: 0.00 0.06) with existing datasets, IndicFake offers an unparalleled benchmark for multilingual deepfake detection. Second, we propose SAFARI- LLM (Semantic Acoustic Feature Adaptive Router with Integrated LLM). This novel frame- work integrates Whisper's 's semantic embeddings and m- HuBERT's acoustic features through an adaptive Audio Feature Unification Module (AFUM). Enhanced by LoRA-fine-tuned LLaMA-7B, SAFARI-LLM achieves unmatched cross-lingual and cross-family generaliza- lion. Evaluations across the IndicFake, DECRO, and WaveFake datasets remonstrate ils superiority, outperforming 14 state-of-the-art models with standout accuracies of 94.21% (English-to-Japanes transfer WaveFake) and 84.48% "English-to-Chhnese transfer DECRO), alongside robust performance across diverse linguistic contexts. These advance- ments establish a new standard for reliable, scalable audio deepfake detection. Resources will be publicly available at: URL
1 Introduction
Voice technology has fundamentally changed how we engage with devices and services. Driven by sophisti- cated speech recognition that converts spoken words into text with remarkable precision, it powers assistants such as Siri, Alexa, and Google Assistant, enabling wide range of tasks through simple voice commands. In the United States, approximately 128 million people use these assistants each month. Meanwhile, smart speaker adoption has increased by 135% since 2018, highlighting the rapid growth of voice-focused tech- nology. Beyond convenience, voice interfaces are becoming increasingly vital for security through speaker verification, utilizing unique vocal features as biometric markers. Financial institutions such as Wells Fargo and Barclays now utilize this technology, enabling secure account management through voice commands. Indian Railways has similarly introduced voice-enabled ticket booking, streamlining travel for millions.
http://tinyni..ca/vvvvscceell http://tinyurl.com http:///wwrrllcma//aaaaaaaaaaaaa

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Published in Transactions on Machine Learning Research (11/2025)
(A)
Marathi
Oriya
Maithili
Hindi
Assomese
Urdu
Punjabi
SAFARI-LLM and IndicFake Benchmark
Bangla
Rajasthani
Manipuri
Gujarati
Boro
Telugu
RQ1: Cross-Lingual Generolization
IndicFake Dataset
Dogri
English
Tamil
Kannada
RQ2: Extended Cross Languuge Family Generalization
Malayalam
2
SAFARI-LLM
Tibeton
Others
3
RQ3: Impoct of Model Architecture
Indo-Europeon
Dravidien
Figure Overview of the IndicFake benchmark. SAFARI-LLM integrates langnage-specific and universal features for robust detection, addressing linguistic diversity. The IndicFake dataset spans 18 languages across Indo-European Dravidian, Sino-Tibetan, and other language families, allowing for comprehensive evaluation. SAFARI-LLM integrates language-specifie and universal features for robust detection, addressing linguistic diversity.
These advancements also present significant risks. A recent report indicated nearly 50 million Al-generated 'voice clone' calls spanning 22 official languages during the two months preceding India's General Elections, highlighting the growing threat of deepfakes exploiting linguistic diversity. Such deepfakes pose severe implications, including the potential for election manipulation, financial fraud, and social engineering attacks. Moreover, the ability of these deepfake technologics to convincingly replicate individual voicos, inchuding those of public figures, adds layers of complexity to authentication and verification processes, necessitaling urgent advancements in detection technologies. Existing studies explore various deepfake algorithms, but real-world complexities remain underexamined, notably the impact of accent variations, dialectal differences, and the robustness of models across linguistically diverse settings (Ranjan et al., 2023 2024
Furthermore, voice-based systems are particularly vulnerable in multilingual contexts, where training data predominantly consists of high-resource languages, resulting in less effective coverage of low-resource lan- guages. Such discrepancies in data availability significantly impact the detection efficacy, highlighting imbalance that undermines the overall security robustness. These gaps emphasize the urgent need for exten- sive and representative datasets to facilitate practical training and evaluation of deepfake detection systems in multilingual and cross-linguistic scenarios.
1.1 Related Work
Over the past decade, numerous voice anti-spoofing datasets have been developed to address deepfake audio challenges. Benchmark datasets, such as ASVspoof (Wu et al.
2015 Wang et al. 2020 Yamagishi et al. 2021
Wang et al. 2024) and Audio Decpfake Detection (ADD) (Yi ct al. 2022 2023), have spearheaded tasks like
Take audio delection and manipulation region localization. However, these datasets generally feature English
samples with limited noise and codec variations (Reimao & Tzerpos 2019 Frank & Schönherr 2021 Ma
et al.
2022), reducing their effectiveness in diverse acoustic scenarios. Datasets such as MLAAD (Müller
et al.
2024 and DECRO (Ba et al. 2023)
expand linguistic coverage but lack partial fakes (Yi et al. 2022
Efforts like HABLA (Tamayo Flórez et al. 2023). ILLUSTON (Thakral et al.) 2025). CVoiceFake (Li et al.
2024), and Voice Wukong (Yan et al. 2024 broaden non- non-English contexts. Yet, no dataset comprehensively
offers extensive multilingual coverage, advanced generation methods, and real-world variability (Table
Algorithnically, early approaches employed handcrafted features, such as phase (iao et al. 2015), magni- tude (Tan et al. 2016), and pitch (Korshunov & Marcel 2016), or deep learning models using raw waveforms
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Table 1: Summarizing the key statistics of the proposed IndicFake dataset and its comparison with existing speech deepfake datasets. IndicFake is the largest among all the existing datasets and covers 18 languages of the Southeast Asian region.
Dataset
Year
Language
Indic Languages
Spoofed Methods
# Total Samples
ASVspoof 2015 Wu al. 2015
2015
English
10
246,500
ASVspoof 2019-1 LA Waang et it
2020
2019
English
X
19
130,378
FoR (Reimao & Tzrpps 2019
2019
English
7
87,285
ASVspoot 2021-LA
Yarnagishi et al.
2021
2021
English
X
19
148,148
ASVspoof 2021-DP Yamagishi et al.
2021
2021
English
X
100+
572,616
WarePake |Fank & Schönherr 2021
2021
English, Japanese
X
7
117,985
ADD2022 LL (Y] al. 2022
2022
Chinese
X
Uuknown
389,419
Latin American Tamayo Flörez et al.
2023
2022
Spanish
X
6
58,000
CFAD Ma et al
2122
2023
Chinese
12
231,00 600
DECRO Ba et al.
2023
2021
English, Chinese
10
118,38
MLAAD Müller of al.
2024
2024
38 Languages
Vilindii Bangha)
26
82,000
VSSspoofo Wang of m
2024
2021
English
32
1,211,186
Speech-Forensics Forensies
al.
2024
2024
English
7,362
IndicFake (Pro (Proposed)
2025
18 Languages
4,222,759
extracted representations (Kawa et al. 2023 Tak et al. 2021a Jung et al. 2022). Although effective
standardized benchmarks, English-trained models falter in multilingual scenarios [Korshunov & Marcel 2016 Müller et al.] )022) or those with accented speech (Ranjan et al. 2024), underscoring the need for cross- wooss-lnguall methods (Ba et al. 2023). Challenges such as partial-truth detection, explainability, and noise robustness (Ranjan et al. 2023) highlight the urgency for comprehensive datasets and unified architectures for reliable real-world deepfake detection.
LLM Reprogramming for Non-linguistic Tasks. Recent work has demonstrated that large pre-trained language models can be reprogrammed to handle non-linguistic modalities by adapting their input or inter- mediate representations, rather than retraining from scratch. Jin et al. (2024) proposed Time-LLM, which reprograms a frozen LLM for time-series forecasting by transforming numerical inputs into token sequences. Melnyk et al. (2023) introduced ReprogBERT, applying a mapping between biological sequences and BERT embeddings for antibody modcling. Similarly, Fan et al. (2024) presented LLMAir, which adapts LLMs for air-quality prediction through task-specific reprogramming. These studies emphasize the LLM''s potential universal sequence modeling engine rather than purely linguistic system perspective adopted in SAFARI-LLM, where the LLaMA backbone serves as multimodal fusion layer integrating semantic and acoustic embeddings for deepfake detection.
1.2 Problem Formulation and Research Contributions
The multilingual audio deepfake detection problem involves classifying an audio sample X E RT (where T is the temporal dimension) as real (y 0) or fake (y = 1) across languages 1 E L. = (4, ...,,,, and generation methods m M. Linguistic diversity introduces complexity, as languages exhibit distinct phonetic, acoustic, and prosodic characteristics. Additionally, varied synthesis methods produce unique artifacts, complicating detection in real-world scenarios.
The objective is to minimize the binary cross-entropy loss:
N by log (olog(ff(())) + (1-w)) log(1 - fo().))
1 N
min (())
(1)
where fo is the detection model parameterized by 0, and N is the number of training samples. However, the assumption that training and testing data share the same distribution often fails in multilingual set- tings, where models trained on one language must generalize to others with differing acoustic and linguistic properties. This paper addresses three key research questions to tackle these challenges:
RQ1 (Cross-Lingual Generalization): Can a model trained on language 1g detect deepfakes in language l. (Jo (1a) - y(b))y?
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This question evaluates whether models can generalize across languages with distinct phonetic inventories, prosodic patterns, and acoustic traits. Success in this task requires learning language-agnostic features, enabling practical deployment where labeled data for every language is unavailable.
RQ2 (Cross-Language Family Generalization): Can a model trained on language family Fa detect deepfakes family Fb (fo X(F.) + y(
This extends RQI more diverse linguistic structures, such as Indo-European versus Dravidian families, which differ in phonological systems, word order, and consonant-vowel patterns. This is critical for regions like India, where multiple language families coexist. Success indicates the model captures universal deepfake artifacts across fundamentally different linguistic domains.
RQ3 (Impact of Model Architecture): How do architectural choices and input representations R E [Rraw, Rspec- Raual] affect detection performance (fe.R : X + y)?
This investigates the role of raw waveforms (Rra)), spectral features (Repec), and dual-stream representations (Ravat) in multilingual detection. It also explores how transformer-based encoders, attention mechanisms, and feature fusion impact robustness. Optimal performance requires co-designing architectures with input representations to capture both temporal and frequency-domatt cues.
We address these research questions with two primary contributions, significantly advancing multilingual audio deepfake detection:
IndicFake Dataset: We introduce multilingual audio deepfake dataset with over 4 million samples across 18 languages from Indo-European, Dravidian, and Sino-Tibetan families. Unlike existing datasets that primarily focus on English or have limited multilingual settings, IndicFake enables robust evaluation of cross- lingual and frmmll generalization (RQ1, RQ2). It includes authentic and synthetic audio from state-of the-art text-to-speech systems, reflecting diverse speakers, acoustic conditions, and generation methods.
SAFARI-LLM Architecture: We propose a novel detection framework combining dual-stream semantic (Whisper) and acoustic (m-HuBERT) encoders via an Audio Feature Unification Module (AFUM). Designed for RQ3, SAFARI-LLM uses LoRA-based lne-tuning and dynamic routing to adaptively integrate somantic and acoustic fcatures, enhancing generalization across languages and generation methods.
SAFARI-LLM achieves 94.21% accuracy in English-tooJJpaaeese transfer on WaveFake, 84.48% in English-to to- Chinese transfer on DECRO, and balanced performance across IndicFake's diverse lan- guage families, demonstrating its effectiveness in addressing real-world multilingual deepfake detec- tion challenges.
2 Proposed IndicFake Dataset
Deepfake detection systems predominantly trained on English audio data struggle when applied to non- English scenarios, emphasizing critical gap in existing multilingual datasets (Wang al. 2020; Yamagishi et al. 2021 Yi et al. 2022 2023 To bridge this gap, introduce IndicFake, extensive multilingual dataset explicitly designed to enhance robust, cross-lingual deepfake delection. IndicPake comprises over 4 million audio samples, covering English and 17 Indian languages from three major language families: Indo-Aryan, Dravidian, and Sino-Tibetan, as detailed in Table 2 The dataset uniqucly integrates authentic speech recordings alongside synthetic audio generated by advanced text-to-speech (TTS) models, offering comprehensive coverage of linguistic nuances and acoustic variations.
2.1 Dataset Construction
Creating a multilingual speech dataset for Indian languages is challenging due to the extensive diversity in scripts, phonetic inventorics, and prosodic structures. For instance, even languages within the same family, such as Hindi and Marathi, share the same script (Devanagari), whereas languages like Bengali and Punjabi utilize entirely different writing systems (Fastern Nagari and Gurmukhi, respectively). Similarly,
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Table 2: Characteristics of the proposed IndicPake dataset. The dataset contains real and synthetic speech samples across English and 17 Indian languages, including per-model TTS splits by gender and overall dataset composition.
MMS
IndiCTTS
DonaLabTTS
DonaLabTTS2
Language
Real Data
Male
Male
Female
Male
Female
Male
Female
Assamese
112,426
30,000
29,982
29,982
26,927
26,927
Bangla
111,077
30,000
29,986
29,986
29,640
29,640
29,653
29,653
Bodo
5,715
22,118
Dogri
3,649
5,499
English
30,000
29,908
29,908
30,043
21,631
Gujarati
144,337
28,885
28,885
29,261
29,261
Hindi
221,022
30,000
29,915
29,915
29,186
27,345
29,508
15,188
Kannada
214,855
30,000
29,995
29,995
22,476
28,793
29,240
29,240
Maithili
328
14,960
Malayalam
153,954
30,000
29,994
29,994
28,851
28,851
28,738
8,778
Manipuri
46,813
4,816
4,815
18,553
Marathi
211,906
30,000
26,306
29,376
29,376
29,429
29,429
Oriya
115,732
30,000
27,319
27,318
29,724
29,722
Punjabi
137,442
30,000
24,751
24,604
30,033
30,033
Rajasthani
4,925
4,926
4,926
4,926
4,926
4,925
Tamil
146,215
30,000
29,920
29,920
25,134
28,284
30,002
30,002
Telugu
259,908
30,000
29,989
28,810
28,810
29,558
29,536
Urdu
112,185
41,335
30,000
30,000
#samples
1,997,564
391,794
327,806
271,363
227,284
234,910
405,595
366,443
#samples/model 1,997,564
391,794
599,169
462,194
772,038
#samples/class
1,997,564
2,225,199
Total
4,222,759
the Dravidian languages (Kannada, Malayalam, Tamil, Telugu) each possess distinctive scripts, and Sino- Tibetan languages (Manipuri, Bodo) frequently adopt multiple scripts (e.g., Eastern Nagari, Devanagari). Recognizing this complexity, our dataset construction approach involves two distinct steps: the systematic collection of authentic speech from diverse linguistic and script backgrounds, and the generation of syuthetic speech samples that capture wide range of tonal and phonetic characteristics.
Real Data Collection: IndicFake derives its authentic speech exclusively from the curated Dhwani corpus (Javed et al. 2022), which provides lists of Creative Commons-licensed YouTube URLs specifically prepared for ASR research. From Dhwani, selected 17 languages that cover the Indo-Furopean (Assamese, Bangla, Marathi, Oriya, Punjabi, Rajasthani, Maithili, Dogri, Urdu, Gujarati, Hindi), Dravidian (Kannada, Malay- alam, Tamil, Telugu), and Sino-Tibetan (Manipuri, Bodo) language families. We sampled 200 CC-licensed videos per language (where available) across the education, news, technology, sports, and finance domains capture a varicty of speaking styles.
All recordings were converted to 16zHz mono-channel format. We applied Voice Activity Detection (py- webrtevad, aggressiveness=2) to remove non-speech segments. We filtered out segments with Signal-to-Noise Ratios below 15 dB using the WADA-SNR method, ensuring consistent audio quality. Each recording was segmented into non-ovellapping ~5-second windows; final clip lengths vary as VAD trims boundaries, and very short segments are discarded. To minimize the risk of synthetic contamination in the real subset, each source vidco underwent independent review by two annotators, following standardized guidelines. Annotators excluded: (a) content explicitly labeled as "TTS." 'AI voice," or "synthetic' in titles /ttss//decrppptsscchaaee (b) dubbed or post-processed material; (c) content. exhibiting synthetic speech markers such as unnaturally
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monotonic prosody, robotic cadence, suspicionsly uniform noise floors. We constructed 70/30 train-test split at the video level to ensure speaker independence and prevent content leakage across partitions, yielding approximately 2,660 hours of verified authentic speech for training and evaluation.
Fake Audio Generation: Synthetic speech samples within IndicFake were generated using multiple so- phisticated TTS models. These models span a variety of architectures, from traditional pipeline-base approaches to state-of-the-art end-to-end frameworks, ensuring comprehensive coverage of synthetic speech charactcristics:
IndicTTS (F01) (Kumar et al. 2022): Utilizes FastPitch Ren ct al. 2019) for efficient mel-
spectrogram prediction coupled with HiPi-GAN (Kong al. 2020 for generating high-fidelity audio waveforms. This combination ensures rapid generation of natural-sounding speech with accurate prosodic modeling.
DonaLabTTS (F02) & DonaLabTTS2 (F03) (Ren et al. 20200: Both models are derived from the FastSpeech2 framework but differ significantly in their phoneme alignment strategies. DonaL- abTTS applies a hybrid segmentation method to achiove robust phoneme-level alignments. At the same time, DonaTabTTS2 utilizes the precise Montreal Forced Aligner (MFA) method, resulting consistently accurate duration modeling across diverse linguistic contexts.
Massive Multilingual Speech (F04) Pratapp et al. 2024): Employs a Variational Inference with adversarial learning for Text-to-Speoch (VTTS) model, directly generating raw waveforms without intermediate spectrogram stages. This approach effectively captures an expansive range of prosodic variations, benefiting from extensive multilingual teeirriinn encompassing up to 1,100 languages.
Table 3: Language metadata across the IndicPake dataset showing language codes, speaker gender dis- tribution, script systems, language families, and native regions, highlighting the dataset's linguistic and demographic diversity.
Language Code Speakers
Script
Family
Native Region
Assamese as male, female
Eastern-Nagari
Indo- Muro-Europeaa
Assam
Bangla bn male, female
Eastern-Nagari
Indo euro-Eu
West- Best-Bengal, Bangladesh
Bodo brx
female English dgo male
Devanagari
Sino-Tibetan
Bodoland Territory
Dogri
Dogri
Indo-European
Rajasthan
male, female
English
Indo-European
Pan India
Gujarati
male, female
Gujarati
Indo- audo-European
Gujarat
Hindi
male, female
Devanagari
Indo-European
Hindi Belt
Kannada kn male, female male Malayalam ml male, female Manipuri mni male, female male, female male, female male, female male, female
Kannada
Dravidian
Karnataka
Maithili ma
Devanagari
Indo- Furopean
Bihar
Malayalam
Dravidian
Kerala
Meetei, Eastern-Nagari
Imphal valley (Manipur)
Marathi mr Oriya
Devanagari
Indo-European
Maharashtra
Odia
Indo-European
Odisha
Punjabi pa
Gurumukhi
Indo-European
Eastern-Punjab
Rajasthani
Tamil raj
Devanagari
auro-European
Rajasthan
male, female male, female Telugu
Tamil
Dravidian
Tamil Nadu
Telugu
Dravidian
Andhra Pradesh, Telangana
Urdu
male, female
Arabic
Indo-Euro European
Hindi Belt
2.2 Dataset Diversity
The IndicFake dataset comprehensive and linguistically diverse collection featuring 18 languages across India's three major language families. This extensive diversity ensures robust cultural and linguistic rep-

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Distribution of Languages
436611
414594 412079
400000
385822
339160
319635
300000
259815
260629
276863
256244
No of audio
213520
200000
14:490
74997
50545
15288
9148
Assames
Bangla
Boro
Dogri
English
Gujarati
Hindi
Kannada
Maithili
Malayala
Manipuri
Marathi
Oriya
Punjabi
Rajastha
Tamil
Telugu
Urdu
Language
Figure 2: Distribution of audio samples across 18 languages, showing representation of major languages and inclusion of low-resource languages.
resentation, essential for effective multilingual deepfake detection. The dataset includes various scripts, reflecting India's rich textual heritage. For instance, the Eastern-Nagari script is employed for Assamese and Bangla, representing linguistic traditions from eastern India. The widely used Devanagari script encom- passes Hindi, Marathi, and Rajasthani, illustrating central and western linguistic characteristics. Southern languages Tamil, Telugu, Malayalam, and Kannada each possess distinctive scripts with unique characters and writing conventions.
Gender representation within the dataset has been carefully curated maintain balanced voice diversity across most languages. Both male and female voices are comprehensively represented, supporting nuanced analyses of gender-specific vocal features. Languages like Dogri, Maithili, and Bodo exhibit single-gender representation due to demographic constraints and data availability limitations in these linguistic communi- ties.
Categorizing languages by family provides essential linguistic context. The Europenn family comprises twelve languages, including Assamese, Bangla, Dogri, English, Gujarati, Hindi, Maithili, Marathi, Oriya, Punjabi, Rajasthani, and Urdu, highlighting the significant diversity within this linguistic group. The Dravidian family, represented by languages such Kannada, Malayalam, Tamil, and Telugu, showcases the distinct linguistic identity of southern India. The inclusion of Sino-Tibetan languages, such Bodo and Manipuri, adds further linguistic depth to the dataset.
Geographically, IndicPake captures linguistic diversity across India, ranging from the northern mountainous regions (Dogri) to the tropical southern landscapes (Malayalam), and from the western states (Gujarati) to the northeastern areas (Assamese). This comprchensive geographic coverage ensures broad cultural repre- sentation, effectively reflecting the linguistic richness and complexity of the Indian subcontinent. Detailed dataset information is presented in Table

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Longuage Distribution in IndicFFike
Gender Distribution IndicFake
Duration Distribution IndicFake
Asserase
*8.0s
0.0s-2.0s
Syjenth 7.50
22.8%
12.4%
Tand
Panjebi
5.0%
Hind 9.5%
Female
48.7%
Male 51,3%
2 2.0s.4. 005-4,00
6.5%
Coreads
4.0s-8.0s
915
5.2%
40, 0%
(A)
(B)
(C)
Figure Showcases the distribution of speaker gender, language, and duration in the IndicFake dataset, demonstrating dataset balance. The language distribution (A) shows balanced coverage across 18 languages. The speaker distribution (B) highlights maintained gender balance across both real and synthetic speech samples, enhancing the dataset's representativeness for deepfake detection research. While the duration distribution (C) indicates natural variation, ranging from 0.5 to over 8 seconds, it reflects real-world speech patterns.
2.3 Dataset Statistics
The IndicFake dataset comprises over 4.2 million speech samples, totaling approximately 7,350 hours of audio data. This extensive collection spans English and 17 Indian languages, grouped into three prominent language families: Indo-European, Dravidian, and Sino-Tibetan, A detailed breakdown of this dataset is presented in Table IndicFake maintains balanced distribution, with most languages exceeding 100,000 samples, while thoughtfully preserving representation for low-resource languages. Specifically, the dataset comprises approximately 2.2 million synthetic audio samples (approximately 4,690 hours) and million real audio samples (approximately 2,660 hours), both generated using four advanced TTS systems.
The language distribution within IndicFake demonstrates deliberate resource allocation to ensure robust representativeness. Major languages such Hindi (414,594 samples), Kannada (412,079 samples), and Telugu (436,611 samples) are well-represented, aligning with their widespread usage and significant speaker populations. Medium-resouree languages, including Malayalam (349,477 samples), Tamil (339,160 samples), and Marathi (385,822 samples), also maintain strong representation, ensuring comprehensive analylical capabilities. Crucially, IndicFake incorporates lower-resource languages, such as Bodo (74,997 samples), Dogri (50,545 samples), and Rajasthani (9,148 samples), highlighting the datasct's inclusive design, which aims support technology solutions across diverse language communities, regardless of their size or resource availability. Figure 2 provides visual overview of the dataset's language-wise distribution.
The duration of audio samples within IndicFake has been carefully curated to encompass range of speech scenarios. Short audio segments (0.8-2.0 seconds) constitute 12.4% of the dataset, effectively capturing brief utterances and quick speech interactions. Medium-length segments (2.0-4.0 seconds) represent typical conversational turns, accounting for 24.8% of the dataset. Longer segments (4.0 8.0 seconds), comprising 40.0%, offer substantial context suitable for detailed analysis. Finally, extended segments exceeding 8.0 seconds make up 22.8%, enabling exploration of longer speech patterns, prosody, and extended conversational contexts.
IndicFake also achieves near-perfect gender parity, with male speakers representing 51.3% and female speakers comprising 48.7% of the total audio samples. This balanced gender representation is essential for developing unbiased audio processing and deopfake detection algorithms that can achieve robust performance across diverse speaker demographics. Figure 3 ratustrates the distribution across languages, durations, and gender, highlighting the dataset's comprehensive and balanced nature.
2.4 Dataset Comparison
To contextualize IndicFake's contribution within the existing landscape of audio deepfake datasets, we con- ducted comparative analysis using Jaccard similarity indices and UpSet plot visualizations. Jaccard simi-
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Table 4: Jaccard similarity indices comparing language overlap between IndicFake and existing audio deep- fake datasets, demonstrating IndicFake's unique contribution language coverage in deepfake detection research.
Datasee
ABVigue! 2015
A8Viguof 2016-1A
Ful ASViguet 2023-LA
ASViquoe 3021-DF
WovePake
ADD2069-12 1P
Latin Amerioan
UPAD
DECRO
ASViguoo
SpredsPurreiis Porneis MLAAD
Jaerand
6.0
0.06
0.06
0.15
(10)
0.00
6.05
6.0
larity indices revealed minimal overlap between IndicPake and existing datasets, ranging from 0.00 to 0.06. IndicFake shares the highest overlap (0.06) with datascts such as ASVspoof 2015, ASVspoof 2019-LA, FoR, ASVspool 2021-LA, ASVspoof 2021-DF; and Speech- Forensics. This notably low overlap underscores In- dicFake's distinctiveness, particularly in terms of language diversity. A detailed comparison using Jaccard indices provided in Table
The UpSet plot visualization in Figure/ offers further insights into the dataset intersections. MLAAD emerges as the most linguistically diverse dataset with 38 languages, closely followed by IndicFake's 's sub- stantial coverage of 18 languages. The most significant intersection occurs between MLAAD and IndicFake, highlighting overlapping coverage of several Indian languages. However, this intersection remains compara- tively small relative each dataset's i total linguistic soope, reinforcing the complementary nature of these resources. Other datascts, such as DECRO and WaveFake, cach intersect minimally, emphasizing their nar- rower linguistic coverage. Most other existing datasets primarily concentrate on English or Chinese, with minimal overlap across languages.
This comprehensive analysis highlights IndicFake's ss unique and significant contribution to linguistic diversity within the field of audio deepfake research. By encompassing numerous underrepresented Indian languages, lake fills a critical gap in existing datasets, establishing itself as a valuable resource for developing more inclusive, robust, and universally applicable deepfake detection technologies.
Overlap of Languages Datasets
20
Table 5: Comparing speech quality met- rics for real and fake andio samples of the proposed IndicFake dataset. SIG: Speech Quality, BAK: Background Noise Quality, OVRL: Overall Quality, P808- MOS: ITU- T P.808 Mean Opinion Score
Speech-Forerdies
Latin Amerrcan
AGApport
ASrppoot 2021-LA
AtVippott 2021-0r
Subset
SIG
BAK
OVRL
P808-MOS
AdVipeer 2039-1A
AGiepost 2015
Real
3.175
3.367
2.650
3.233
LD02022-LP
Fake
3.440
4.111
3.190
3.879
IndiAkkk
Figure 4: UpSet plot visualizing the intersection of lan- guages across audio deepfake datasets. The plot reveals limited overlap between datasets, with MLAAD (38 lan- guages) and IndicFake (18 languages) showing the highest language diversity.
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2.5 Dataset Quality
The quality evaluation of the IndicFake dataset provides essential insights into the perceptual characteristics of real and synthetic audio samples. We employ four key metrics: Speech Quality (SIG), Background Noise Quality (BAK), Overall Quality (OVRL) (Reddy et al.) 2022), and the ITU-T P.808 Mean Opinion Score (MOS) Reddy et al. 2021 5
these results. Synthetic audio samples demonstrate superior performance in background noise quality (BAK: 4.111 synthetic vs. 3.367 real) and overall quality (OVRL: 3.19 synthotic vs. 2.650 real), indicating effective noise suppression. This aligns with prior DNSMOS findings [Redy al. 2021 2022 confirming that noise reduction significantly enhances perceived audio quality.
The 8.3% improvement in speech clarity for synthetic samples (SIG: 3.440 vs. 3.175 for real) suggests syn- thetic audio effectively maintains phonetic clarity. However, subtle artifacts remain detoctable, particularly during specialized analyses. The higher MOS scores (3.879 synthetic vs. 3.233 real) further confirm synthetic andio's s human-like perceptual quality, mirroring observations in multilingual deepfake detection research.
These results present a dual challenge for detection systems. Synthetic audio achieves a quality sufficient deceive casual listeners, as evidenced by elevated MOS and OVRL scores; yet, it retains identifiable artifacts detectable through structured analysis. Notably, the BAK metric underscores significant improvements in noise suppression (21.3% increase). In contrast, the narrower margin in SIG (8.3% improvement) highlights advancements in phonetic fidelity but points toward lingering subtle synthetic artifacts. This quality paradox highlights the need for detection methods that focus on residual artifacts, rather than relying solely on con- ventional quality indicators. IndicFake's detailed quality evaluation thus offers a comprchensive framework to drive the development robust deepfake detection systems.
2.6 Dataset Protocol
To facilitate rigorous and systematic evaluations, IndicFake is structured into three distinct subsets. Set A encompasses ten Indo-European languages: Assamese, Bengali, Dogri, Gujarati, Hindi, Maithili, Marathi, Odia, Punjabi, and Urdu. Set B includes four Dravidian languages: Kannada, Malayalam, Tamil, and Telugu. Set comprises Bodo, Manipuri, and English.
For Sets and B, we implement train-test splits to ensure speaker and model independence. Training datasets contain synthetic samples from DonaLabTTS2 and MMS TTS, while evaluation datasets include synthetic samples from DonaLabITS and IndicTTS, facilitating evaluation of unseen TTS models. Ad- ditionally, real speech data is partitioned to maintain speaker independence and avoid biases. Set C is exclusively designated for cross-lingual generalization testing, featuring languages entirely unseen during training. This struct ured protocol supports comprehensive evaluation across three dimensions: cross-model, cross-language, and speaker generalization, thereby establishing robust benchmarks for multilingual deepfake detection systems.
2.7 Dataset Spectral Analysis
To understand the spectral characteristies of synthetic speech in IndicFake, we conducted a detailed frequency analysis. Figure 3
illustrates average energy distributions across frequency bands, alongside difference plots highlighting deviations from natural speech. The spectral profiles of real audio reveal typical characteristics, with prominent energy concentrated in lower frequencies (0-3 kHz) and gradual declines at higher frequencies. Synthetic audio generated by MMS, IndicTTS, DonaLabTTS, and DonaLabbTSS maintain similar overall spectral shapes, but exhibit notable deviations, particularly within higher frequency bands (6 11 kHz).
Difference plots quantify these spectral deviations explicitly. MMS audio exhibits the most significant high- frequency artifacts, showing variations of up to 100 dB compared to natural speech. DonaLabTTS2 achieves improved spectral fidelity over its predecessor, especially in mid-range frequencies (3 6 kHz), though some discrepancies persist at higher frequencies. ITd maintains more consistent spectral behavior but still exhibits notable deviations above kHz.
These characteristic spectral differences between synthetic and natural speech provide reliable indicators for deepfake detection systems. Persistent high-frequency artifacts across all TTS systems suggest fundamental
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Difference MMS & Real Audios
Real Audios
MMS Audios
Grogo
(H40)
Difference IndicTTS
Real Audios
MMS Audios
Real Audios
Imesenty Bale
Imequnnyy delet
Froqutecy drib)
Difference DonaLabTTS & Real
Real Audios
MMS Audios
Trcseror 801
Frssuinc Ortal
Difference DonaLabTTS2 & Real Audios
Real Audios
MMS Audios
Duffemes Beallandond: Bealltendos
2ing Sbowy
Figure 5: Spectral comparison between real and synthetic speech across different TTS systems. Fach row shows the average energy distribution (dB) across frequency bins for real speech (left), synthetic speech (right), and their difference (center), highlighting characteristic deviations in the high-frequency region (6-11 kHx). The last plot shows the difference between two random real sets. These plots are inspired by the Wavefake Caper(rrank & Schönherr 2021
limitations in current synthetic speech generation methods. These insights highlight both opportunities enhance synthetic speech quality and strategies to improve deepfake detection techniques.
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Real
Fake
MLP
LORA Adapters
Large Language Model
Audio Feature Unification Module
Routing Weights
(AFUM)
Audio Feature Router
Feature Experts
Feature Experts
Semantic Adapter
Acoustic Adapter
Whisper
m-HuBERT
100
Frozen Trainable
Figure 6: Overview of the proposed approach. Two speech encoders and adapters with different focuses are utilized, where Whisper and its corresponding adapter used for extracting semantic information, and m- HuBERT is used for extracting acoustic information. Before being fed to the LLM, these two representations are concatenated together.
3 Proposed SAFARI-LLM
The proposed SAFARI-LLM (Semantic Acoustic Feature Adaptive Router with Integrated LLM) ad- dresses three key research questions: cross-lingual generalization (RQ1), extended cross-language family generalization (RQ2), and the impact of model architecture on performance (RQ3). SAFARI-LLM inte- grates semantic and acoustic speech processing with a Large Language Model (LLM) to enable robust, multilingual deepfake detoction across diverse linguistic contexts. As depicted in Figure 6 SAFARI-LLM employs a dual-stream architecture comprising two specialized encoders: Whisper (Radford al. 2022) for semantic analysis and m-HuBERT (Boito et al. 2024) for acoustic profiling. Their outputs are fused using Audio Feature Unification Module (AFUM), which dynamically balances semantic and acoustic features. The unified representation is then processed by an LLM, fine-tuned with Now-Rande Adaptation (LoRA), to achieve high detection accuracy across varied linguistic settings.
3.1 Dual-Stream Speech Encoders
The dual-stream architecture addresses RQ1 and RQ2 by capturing both semantic and acoustic information critical for effective cross lingual and cross-language family deepfake detection. Whisper-large (Radford et all 2022), pretrained on 96 languages, extracts high-level semantic content from audio inputs, enabling robust generalization across languages. Concurrently, m-HHuBERT--asee (Boito et al., 2024), pretrained on 147 languages, captures fine-grained acoustic features, including speaker identity, timbre, and prosodic patterns, which are essential for detecting subtle deepfake artifacts.
cross-lingual
Formally, given a batch of audio signals X RxT, where B is the batch size and T is the temporal dimension, first transform each signal into log-mel spectrogram S BxFT where F denotes the
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frequency dimension. Semantic embeddings computed as:
H.- Whisper(S), H, E BxTxxD
(2)
Where Tg is the temporal dimension of the semantic features, and D, is the embedding dimension. Acoustic embeddings are derived using n-HuBERT:
H mHuBERT(X)), H. E BRXT.D
Where To and D. represent the temporal and embedding dimensions of the acoustic features, respectively, To unify these heterogeneous embeddings, we employ adapter modules that perform the following operations:
1. Apply two 1D convolutional layers to reduce dimensionality and align temporal resolutions between H, and Ha.
Utilize bottleneck adapter (Houlsby et al. 2019 to balance computational efficiency and feature expressiveness.
3. Project both embeddings into shared dimensional space using linear layer.
The adapted embeddings, Hy and Hy are mapped to common space:
H H' e R Bx38x1024
(4)
These embeddings are concatenated to form a unified input:
Xm H Hy,, Xm BB
(5)
3.2
Audio Feature Unification Module
The AFUM addresses RQ3 by dynamically balancing the contributions of semantic and acoustic features to optimize detection performance. AFUM comprises K projection experts {Px), each implemented transformer-based layer, and multi-layer perceptron (MLP) Audio Feature Router R (Puigcerver et al., 2023 This design enables adaptive feature weighting, ensuring that the model prioritizes relevant informa- tion based on the input audio characteristics.
Given the concatenated input Xm E BXLxD where L 3 and D = 2048, AFUM computes a unified representation as weighted sum of expert outpuls:
Xm
Wm,k Px(x)).
(6)
where Wm.k are the routing weights for the k-th expert, and Pa (x(m denotes the output of the k-th projection expert. The routing weights are computed dynamically by the router R:
W. = (x(,.))) Wm E BXLLK
(7)
Where (-)) the softmax function, ensuring that the weights are normalized across the K experts for cach input token. This mechanism enables AFUM to adaptively emphasize either semantic or acoustic features based the input, thereby enhancing robustness across diverse linguistic contexts.
"The temporal dimension is directly adopted from Whisper model's semantic embeddings, and the acoustic embeddings aligned to this dimension.
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3.3 Large Language Model (LLM) Integration and Classification
To integrate semantic and acoustic embeddings with multilingual priors and loug-range allention, use LoRA-adapted LLaMA-7B backbone as sequence integrator over audio embeddings. The LLM operates on the unified sequence xm output by AFUM-formod from Whisper (semantic) and m-HuBERT (acoustic) embeddings after their respective adapters- not on text tokens or transcripts. This design enables the backbone model token-level cross-modal dependencies and alignments between semantic content. and its acoustic realization.
Instantiation and adaptation. We instantiate the backbone with (LaMA-7B (Touvron et al. 2023 enhanced via Vicuna instruction-followini fine-tuning (Vicuna 2023). and adapt it efficiently using Low- Rank Adaptation (LoRA) (Hu et al. 2021). LoRA adapters with rank r=8 and scaling a=16 are inserted into the query and value projections of all self-attention layers; the base transformer weights of LLaMA-7B are kept frozen, preserving pretrained priors while enabling task-specific adaptation.
The unified embeddings X from AFUM are processed by the LoRA-adapted LLM:
YLLM (AMMORRAmm))
(8)
The resulting embeddings fed into Multi-Layer Perceptron (MI.P) for binary classification:
i (ML.(()L))))
(9)
where a-) is the sigmoid activation function, producing a probability score for the binary classification task (real vs. fake audio).
We adopt a LoRA-adapted LLaMA-7B model as pretrained multilingual sequence inte- grator whose long-range attention enables effective fusion of semantic (Whisper) and acoustic (m-HuBERTT) embeddings. Our motivation for incorporating an LLM is not to exploit its linguistic knowledge or text generation capabilities, but rather to leverage general sequence modcling capacity to align heterogencous modalities. This design choice consistent with the emerging paradigm of LLM reprogramming, which demonst.rats that pretrained language models can be repurposed for non-linguistie domains such as time- series forecasting biological sequence modeling by adapting inputs into the model's latent space Jiin et al. 2024h: g Fan et al. 2024): Meinyk et al. (2023)
Why an LLM?
In this view, the L.M serves as a flexible and high-capacity transformer backbone for multimodal integration, rather than acting as a linguistic expert. The LoRA fine-tuning enables efficient task-specific adaptation while preserving the general cross-domain priors that promote robust alignment between content and acoustics across languages and deepfake synthesis methods.
4 Experimental Setup and Protocols
This section outlines the experimental setup, detailing the datasets used, baseline models for comparison, implementation specifics, evaluation metrics, and the structured protocols designed to comprchensively assess our proposed SAFARI-LLM model.
Existing Datascts Apart from the proposed IndicFake corpus, we evaluate our method using two promi- nent multilingual datasets to thoroughly examine cross-lingual and cross-Synthesis generalization capabilities:
DECRO (Ba al. 2023): Contains English and Chinese subsets, with 21,218 bona fide Chinese samples and 12,484 English samples, each with predefined training, development, and evaluation partitions.
WaveFake (Frank & Schönherr 2021 ) Features 136,085 samples, including 121,085 English
and 15,000 in Japanese, designed explicitly for assessing multilingual generalization and synthesis variability. We follow the leave-one-out protocol enforcing evaluation unseen generator. For
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Table 6: Comparison of deepfake detection performance across English and Japanese languages using the
WaveFake dataset, showing cross-lingual generalization capabilities for different model architectures. Ace: Accuracy (%), EER: Equal Error Rate.
Train on English,
Train on English,
Train on Japanese,
Train on Japanese,
Models
Eval on English
Eval Japanese
Eval
English
Eval on Japanese
Acc
EER(%)
Acc
(ER(%)
Acc
EER(%)
Acc
EER (%)
Whisper MesoNet
10.82
37.62
33.33
43.80
89.18
46.40
66.67
44.89
MesoNet
89.18
0.57
66.67
3.06
89.74
15.72
79.05
5.74
MSLModel
89.47
19.64
66.67
41.25
89.18
51.87
100.00
0.00
Whisper SpecRNet
89.83
24.46
67.23
33.47
50.45
36.97
89.97
6.38
Whisper LCNN
92.29
14.62
72.76
31.03
16.60
36.55
87.97
12.76
Conformer
93.53
8.96
54.92
45.78
89.18
46.03
99.98
0.01
RawNet2
99.79
0.26
66.65
48.84
18.71
42.74
99.70
0.18
SpecRNet
99.80
0.01
84.85
3.30
52.30
6.84
99.77
0.00
RawGAT-ST
99.85
0.24
87.42
8.36
57.49
19.85
99.01
0.34
AASIST
99.95
0.08
89.27
6.86
12.05
27.80
91.51
0.71
RawBMamba
99.98
0.03
83.25
2.87
37.26
14.00
99.94
0.04
LCNN
99.98
0.02
90.92
8.27
10.82
18.01
99.95
0.06
RawNet3
99.99
0.03
86.67
12.10
80.53
32.06
98.93
0.91
Shisper-frontedd-Ile
99.98
0.02
85.09
1.28
80.48
2.08
99.92
0.03
Proposed
99.99
0.02
94.21
2.48
92.31
5.31
100.00
0.00
both English and Japanese, MB-MeIGAN is excluded from the training process. Evaluation spans MelGAN, MelGAN(L), FB-MeIGAN, MB-MeIGAN (unseen), HiFi-GAN, WaveGlow, PWG, and TTS, jointly testing model and linguistic gencralization.
We benchmark SAFARI-LLM against a comprehensive set of 15 baseline architectures, categorized based on their imput modalities:
Baseline Models
Raw Waveform Models:
Including RawBMamba Chen et al. (0024)
Conformer Rosello et al. RawGAT-ST Tak et al. which operate directly
(0023).
SSLModel Tak et al. (2022). AASIST weon Jung et al.
2022a
(2021c).
RawNet2 Tak et al. 22221b) and RawNet3 weon Jung et al.
2022b
on time-domain signals.
Spectrogram-based Models: LCNN [Wu et al. 2018). Meso.et [Afchar et al. 2018 (specifically
the Mesolnception-4 variant), and SpecRNel (Kawa el. al. 2022a 2023) domain spectrograms.
which process frequency-
For spectrogramn-based baselines, we test standard cepstral features (LFCC and MFCC), as well as advanced embeddings from the Whisper encoder, alone and in combination with cepstral features, inspired by insights from Kawa et al. (2022b).
Evaluation Protocols
Our evaluation protocols are explicitly structured around the three primary RQs:
Cross-Lingual Generalization. We train models one language and test on another within the same dataset, utilizing WaveFake (English-Japanese) and DECRO Singlish-Chinsse These experiments specifically measure each model's capacity to detect deepfake audio across distinct linguistic domains.
RQ1:
RQ2: Extended Cross-Language Family Generalization. To examine generalization across funda- mentally different language families, we use subsets from IndicFake: Set Undo-EEropeaat and Set B (Dravidian). We conduct bi-dircctional experiments, training on one family and testing on the other. We maintain speaker and synthesis-model independence by employing different TTS models DonaLabTTS2 and MMS TTS for training, and DonaLabTTS and IndicTTS for evaluation.
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Table 7: Equal Error Rate (EER) of the SAFARI-LLM on different subsets (LFCC). We train a new model for each data set and compute the EER.
LJSpeech
JSUT
Training Set
MelGAN
MeGAN (L)
MB-MclGAN
FB-MeIGAN
HiFi-GAN
PWG
WaveGlow
MB-MeiGAN
PWG
MeIGAN
0
0.001
0.119
0.333
0.343
0.175
0.12
0.005
0.232
0.021
MeIGAN (E)
0
0
0.313
0.551
0.512
0.398
0.161
0.159
0.012
MB-MeIGAN
0.007
0.014
0.001
0.027
0.129
0.027
0.06
0.075
0.03
FB-McGGAN
0.003
0.004
0.002
0.003
0.04
0.02
0.011
0.043
I5Fi-GAN
0.127
0.145
0.258
0.333
0.008
0.145
0.017
0.299
0.094
PWG
0.507
(1555
0.495
0.704
0.468
WaveGlow
0.044
0.134
0.147
0.378
0.055
0.34
Table 8: Comparison of the proposed model with different baseline models under the single training set
protocol.
Training Set
WaveFake GMM (Table 2
WaveFake RawNet2 (Table 3
SAFARILLIM
McIGAN
0.215
0.292
0.135
MeIGAN (L)
0.222
0.258
0.211
MB-MelGAN
0.108
0.357
0.042
FB-MeIGAN
0.062
0.363
0.014
HiFi-GAN
0.089
0.319
0.161
PWG
0.124
0.358
0.428
WaveGlow
0.085
0.294
0.221
Best aggregated EER
0.062
0.258
0.014
RQ3: Architectural Design Impact. We evaluate the influence of architectural choices and input representations by comparing five categories of models: LLM-based (our SAFARI-LLM), State Space Models (RawBMamba), Graph Neural Networks (AASIST, RawGAT-ST), Convolutional Neural Networks (RawNet2, RawNet3), and Transformers (SSLModel, Conformer). We also analyze performance variations between raw waveform and spectrogram input representations.
Implementation details and metrics: All audio is resampled to 16 mono. Spectrogram-based baselines use a 400-sample window with a 160-sample hop. LFCC features use 128 coefficients with A and AÁ; for some baselines, these cepstra are concatenated with Whisper embeddings. We use the Whisper-large variant throughout. For SAFARI-LLM, we adopt LLaMA-7B (Touvron et al. 2023 with LoRA adapters (r=8, a=16). AFUM employs K=2 transformer projection experts (eight layers each; ~88M parameters). Adapters operate at fixed temporal stride of 80 ms, and the unified token dimensionality is D=2048. Since LLaMA-7B uses hidden size dL1M=4096,
insert learned input adapter Wi, E 22204xx 4096 before the LLM. Models are trained with AdamW (31 =0.9, B3 =0.95, weight decay 0.1). We report Accuracy and Equal Error Rate (EER).
Parameter and efficiency accounting: We fine-tune Whisper-large and m-HuBERT end-to-end, while keeping the LLaMA-7B backbone frozen except for LoRA adapters. Concretely, the trainable components are: (i) Whisper-large, (ii) m-HuBERT, (iii) AFUM, (iv) the stream adapters, (v) the 2048-+4096 input adapter for LLaMA-7B, (vi) LoRA weights on the LLM, and (vii) the final MLP classifier. We show the parameter count of model components in Table 14
Reproducibility. All the resources will be available at the project page
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Table 9: Cross- lingsll deepfake detection results on the DECRO dataset between English and Chinese
languages, demonstrating model performance when trained and evaluated across different language pairs.
Train on English,
Train on English,
Train on Chinese,
Train on Chincse,
Models
Eval on Chinese
Eval on English
Eval on Chinese
Eval on English
Acc
FER(%)
Acc
EER(%)
Acc
EER(%)
Acc
EFR((%
RawNet3
62.57
33.94
81.54
17.25
97.33
2.41
78.91
18.60
Whisper-Mesonee
66.30
21.82
72.09
15.91
66.43
7.78
72.25
21.27
RawGAT-ST
67.79
38.76
84.29
20.65
99.42
0.56
80.79
21.90
AASIST
68.29
32.49
84.56
16.52
98.47
1.17
82.22
9.53
RawNet2
68.74
32.48
84.74
17.16
98.44
1.59
84.41
11.02
Whisper-SpecRRNee
69.60
28.86
83.55
15.78
95.16
4.31
77.79
18.60
Whisper-LCNN
71.04
29.49
85.06
15.72
94.86
4.83
78.44
11.60
RawBMamba
71.49
29.17
86.12
14.93
98.33
1.56
81.90
17.60
LCNN
72.59
25.59
86.64
14.92
99.34
0.72
76.92
22.56
Conformer
72.86
42.89
86.55
22.38
98.23
1.52
76.54
20.66
Whisper-frontee Trontend-LCNn
80.65
25.83
90.57
14.62
98.20
0.96
77.36
8.72
SpecRNet
82.64
22.44
91.53
11.88
96.01
1.74
77.34
16.16
SSLModel
82.72
27.26
91.57
16.17
98.85
1.21
82.15
18.59
MesoNet
83.09
18.40
91.63
10.01
58.30
3.42
54.18
14.07
Proposed
84.48
21.20
92.44
11.35
99.60
0.36
82.70
11.80
5 Results and Analysis
This section presents comprehensive evaluation of SAFARILLLM's performance, addressing our three re- search questions related to cross-lingual generalization, exlended cross-language family generalization, and the impact of model architecture. We report key metrics, compare SAFARI-LLM against baseline models, and embed detailed analyses and inferences within each subsection to elucidate trends and implications for multilingual deepfake detection.
5.1 Cross-Lingual Generalization Analysis (RQ1)
We tested SAFARILLLM's cross-lingual generalization on the WaveFake and DECRO datasels. On Wave- Fake, SAFARI-LLM achieves near-perfect within-language detection: 99.99% accuracy (0.02% Equal Error Rate, EER) for English and 100% accuracy (0% EER) for Japanese, as shown in Table On DECRO (Table g performance remains strong but reveals asymmetries, with 99.59% accuracy (0.36% EER) for Chinese compared to 92.43% accuracy (11.34% EER) for English. This discrepancy stems from dataset imbalances, with Chinese subsets having real-to-fake ratio of 1:2 versus 1:3.4 for English, leading to higher false positives in English detection.
Cross-lingual evaluations highlight the challenges of language transfer. Training on the larger English Wave- Fake dataset (121,085 samples) yields robust generalization to Japanese (15,000 samples), achieving 94.21% accuracy (2.48% EER). Conversely, Japaniso-to-English transfer results in 92.31% accuracy (5.31% EER), suggesting that larger, diverse training data enhances lross-lnngual robustness.
We also evaluate SAFARI-LLM under the single-training-set Fake paper (Table 2). In this setting, the model is trained protocol exactly as described in the Wave- generator (row) and evaluated across all other generators (columns). The results for SAFARI-LLM in this protocol shown in Table 7 and Table M SAFARI-LIM yields substantially lower aEERs for MclGAN, MB-McIGAN, and FB-MeIGAN train- ing, demonstrating stronger in-distribution and cross-vocoder generalization. FB-MelGAN training achieves an aEER of 0.014, compared to 0.062 (GMM) and 0.363 (RawNet2). These improvements suggest that SAFARI-LLM captures spoofing artifacts in a more transferable manner across vocoders, outperforming tra- ditional GMMs and RawNet2 in most cases. SAFARI-LLM's performance weaker on PWG and WaveGlow compared to GMM, which we attribute to vocoder-specifie biases.
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Table 10: Results with training models on Set A Lndo-Euuropent languages) and evaluating across Set A (within-familyy) Set B (Dravidian lan- guages), and Set C (mixed languages) for Indic- Fake dataset, showing cross cross-languagg family gen- eralization.
Train on Set A.
Train on Set A. Eval
Train on Set A. Evad Set
Modell
Eval
EER (%)
EER (%)
EER (%%
RawBMamba
2.217
4.155
7,800
MesoNet
2.746
2.244
8.545
Whisper SpecRNet
3.054
3.164
4.777
Whisper Nes
5.554
3.456
3,828
LONN
6.151
9.968
22.730
Modee
6.742
8.213
17.544
Conformer
RawNeet2
8,881
6.786
8.177
6.132
13.708
15.335
Figure 7: DET curves showing model performance when trained on Set A of the IndicFake dataset and evaluated on Set A, Set B, and Set C test sets.
Proposed
0.941
1.153
0.023
On DECRO, Chinesh-to-English transfer yields 82.69% accuracy (11.79% EER), while Eiglish-to-Chinsss achieves 84.48% accuracy but with a higher EER of 21.19%, indicating sensitivity to language-specific acous- tic characteristics, particularly in prosodic and phonetic patterns.
SAFARI-LLM's dual-encoder architecture, combining Whisper's semantic embeddings and m- m-HHBERR's acoustic features, significantly outperforms single-stream models in cross-lingual settings. For instance, it surpasses RawNet3 by 19.98% in Fngliishto-Jaaaneee accuracy. However, the elevated EER in cross-lingual scenarios (e.g., 21.19% for English-to-Chine facts. These results suggest that while semantic features facilitate robust generalization, acoustic variations across languages continue to pose a challenge. Future improvements should incorporate explicit phonetic modeling and balanced multilingual datasets to reduce false positives and enhance transferability, thereby ensuring the suitability of SAFARI-LLM for real-world multilingual deployment.
suggests residual sensitivity to language-specifi acoustic arti-
The higher EER for English+ Chinese (21.20%) compared to Chinese-English (11.0%) on DECRO high- lights residual language-specific artifacts and distributional mismatch. In contrast, within the Indic setting, where training and target languages are more closely aligned phonetically, cross-family EERs are much lower (e.g., Set A-B: 1.15%, Set B- 3.78%); and joint training on AUB yields 0.72 0.72/0.73% 73% EER on Sets A/B and 0.68% on unseen Set C. Together, these findings suggest that typological proximity and training coverage reduce score-distribntion shift, while remaining crrors motivate prosody-awaro cucs and light language-conditioeed calibration.
5.2 Extended Cross-Languas Family Generalization (RQ2)
We assessed SAFARI-LLM's generalization across language families using the IndicPake dataset, comprising Indo- Ando-European (Set A), Dravidian (Set B); and mixed languages (Set C). The results reveal several critical insights into cross-family transfer capabilitics and architectural performance patterns.
5.2.1 Training on Set A (Indo-European Languages)
When training on Set A, SAFARI-LLM achieves strong in-family performance al 95.12% accuracy (0.94% EER) as shown in Table [O) This performance demonstrates excellent calibration, with the model achieving high accuracy while maintaining excoptionally low error rates. Notably, while bascline models like AASIST achieve higher accuracy (97.49%), their significantly higher EER (1.47%) indicates imbalanced class-specific performance, suggesting potential overfitting to the training distribution.
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Table 11: Model performance when trained Set B (Dravidian languages) and evalu- ated on Set A (Indo-European). Set. B (within- family), and Set C (mixed languages) for Indic- Fake dataset, demonstrating cross- cross-language fam- ily transfer capabilities.
Train on Set B,
Train on Set B,
Thain on Set B, Eval Set
Modbels
Eval Sel.
Eval
EER (%)
EER (%6
EER (%)
LCNN
4.507
3,215
25.597
Ram/BMaaba
5.610
5.416
26.731
Whisper Net
5.676
4.175
19.298
Modee
7-162
5.901
18.246
Whisper MesoNet
7.966
4.821
7.689
Conformer
8.212
9.302
16.452
RawNNet2
8.921
9.496
24.740
Figure DET curves showing model performance when
MesoNet
14,568
9.531
30.521
Proposed
3.728
3.782
7.224
trained on Set B of the IndicFake dataset and evaluated on Set A, Set B, and Set C test sets.
The cross-family generalization results are particularly compelling. Testing the Set A-trained SAFARI-LLM Set B yields 88.17% accuracy (1.15% EER), demonstrating robust cross-family transfer despite funda- mental linguistic differences between Endo-Europcan and Dravidian language familics. This represents only a 6.95% accuracy drop with a minimal 0.21% EER increase, indicating excellent preservation of discriminative features across language families.
In contrast, other models show more dramatic performance degradation. For instance, Whisper MesoNet achieves higher familyy accuracy (96.48%) but suffers from a substantially worse EER (3.45%), repre- senting 2.1x increase in error rate compared to SAFARI-LIM. This pattern suggests reduced reliability and potential overfitting to acoustic patterns specific to the training language family. We show the DET curve for each of the settings in Figure 7
5.2.2 Training Set B (Dravidian Languages)
Training Set B reveals asymmetric transfer capabilities. SAFARI-LLM achieves 86.77% accuracy (3.78% EER) for in-family performance and maintains stable cross-family performance on Set A at 86.77% accuracy (3.72% EER). The remarkably consistent performance across both sets (86.77% accuracy) with nearly iden- tical EER values (3.78% vs 3.72% 3.72%) suggests that the model successfully learns language-agnostic features when trained on Dravidian languages.
However, a critical asymmetry emerges when comparing the effectiveness of Set A and Set B training. The Set A-trained model significantly outperforms the Set B-trained model on Set C (97.30% vs 60.69% accuracy), representing 36.61% performance gap. This substantial difference indicates that Indo-European languages provide more transferable semantic and acoustic cues, likely due to their broader representation in pretrained foundation models like Whisper and m- m-HuBERT. We show the DET curve for each of the settings in Figure
5.2.3 Joint Training Analysis
Joint training on Sets A and B (Table 12 achieves balanced performance: 83.92% accuracy (0.72% EER)
on Set A and 84.64% accuracy (0.73% EER) on Set B. The near-identical EER values (0.72% vs 0.73%) and similar accuracy levels demonstrate successful knowledge integration across language families. This represents an 11.2% accuracy decrease from Set A-only training but achieves a much better balance, with only a 0.48% accuracy difference between families. Importantly, joint training dramatically improves Set C performance, achieving 60.45% accuracy (0.68% FER), which substantially purforrsm Set B-only training
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Table 12: Results from joint training on Set A and Set B, showing how combined training on Indo-European and Dravidian languages af- fects model performance across different lan- guage families.
Train All, oa Set
Trin All
Train All. Eval
Models
on Set
EER (%)
EER (%%)
EER (15)
LONN
1 .232
0.892
6.486
ROwGAT-ST
AASIST
1.505
0.726
3.110
Whisper-Poe Whispet-Pooutend--eee PreetendSpeeRNet
1.535
1.4501
11.688
RawHMarkk
3.224
Whisper Spe
2.529
2.641
0.707
SpecKNet
2,507
1.483
111211
Whisger LCNN
Conformer
3.511
2,020
10.828
MesoNes
3.915
2.5022
14.409
Figure DET curves showing model performance when
RawNet2
13.087
SSIMode
5,003
3.652
12.131
jointly trained on Set A and Set B of the IndicFake dataset and evaluated on Set A, Set B, and Set C test sets.
Whisper MesoNee
6.417
4.180
3,009
Proposed
0.725
0.729
0.680
(60.69%) while maintaining the excellent calibration characteristics of SAFARI-LLM. We show the DET curve for each of the settings in Figure g
5.3 Impact of Model Architecture (RQ3)
We analyzed SAFARI-LLM's architectural contributions compared to baseline models. Raw-audio models like RawNet3 achieve near-perfect within-language accuracy (99.98%) but deteriorate sharply in cross-lingual settings (e.g., 75. 73.23% accuracy for English-to-JJaanese) indicating a strong dependency on language-specific acoustic features. Spectrogram- based models, such as "hhisperfronteed-LCC show lower within-languue accuracy but greater cross-lingual stability (85.09% Englishh-o-Japaness, 80.48% Japanoso-to-English) ben- efiting from language-agnostic pretrained embeddings.
SAFARI-LLM integrates the strengths of both approaches through its dual-stream architecture, leveraging Whisper for semantic features and m-HuBERT for acoustic cues. The Audio Feature Unification Module (AFUM) dynamically balances these representations, achieving a synergy that bridges cross-lingual gaps. For example, SAFARI-LIM outperforms RawNet3 by 19.98% and Whisper-ironenddLLCNN by 9. 9.12% in English-to-Japanees accuracy.
The LoRA-adapted LLaMA component in SAFARI-LLM strengthens multimodal integration by effectively aligning semantic and acoustic embeddings. This loads to consistent performance across languages and spoofing techniques. Importantly, the improvements appear to stem not from the linguistic pretraining of the LLM, but from its generalized ability to model dependencies across heterogeneous feature spaces.
This interpretation aligns with recent findings from LLM reprogramming literature [Jin et al. 2024
; Fan
et al. (2024); Melnyk et al. (003))), which view pretrained language models universal sequence processors that can be adapted to new modalities through minimal parameter updates. SAFARI-LMM adheres to this principle: the LLM acts as multimodal sequence integrator, facilitating the adaptive fusion of semantic and acoustic features while mitigating language-specific biases. Future work could explore more explicit reprogramming strategies and dynamic routing in AFUM to further enhance cross-domain generalization and resilience to emerging deepfake synthosis techniques.
5.4 Ablation Studies
To better understand the contributions of data scale and model design, we conduct two complementary ablations: (i) scaling training data within language, and (ii) isolating the role of each feature stra and
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Gains differ by language family and representation in the pretraining pool.
Table 13: Within-language EER(%) as training samples increase.
Training Samples
Hindi EER
Tamil EER
10k
3.91
4.23
20k
2.35
3.74
Table 14: Ablation (EER %): contribution of the LLM and dual-stream fusion. Base retains both encoders
and AFUM, replacing the LLM with 2-layer MLP head to isolate the LLM's effect. All systems are trained
Set A U Set B and evaluated individually Set A, Set B, and Set C. We also show the parameter count for each of the configurations.
Model Varlant (Train Set AUSet B)
Parameter Count
Eval Set
Eval Set B
Eval Set C
Base (dual-stream + AFUM, MLP head; no LLM)
893,446,
6.532
15.405
23.688
Whisper + AFUM LLM
7,533, 16,,,611,777
4.762
11.079
21.097
m-HuBBRTT 1 APUM LLM
0.988,0755200 204
5.818
13.362
21.970
SAPARI-LLM (Whisper + m-HuBERT AFUM + LLM)
2,631,861,88
0.725
0.729
0.680
the LLM backbone. These controlled studies provide concrete evidence for the effectiveness of SAFARI-LLM.
5.4.1 Effect of Scaling Within a Language
We examine the effect of increasing training data for two typologically distinct Indic languages Hindi (Indo- European) and Tamil (Dravidian). The evaluation is performed in within-language setting, isolating the impact of additional data without tross-lingual transfer. We find that increasing Hindi data from 10k to 20k samples reduces EER by 440% (3.91 -2.35), while Tamil improves by only 12% (4.23 -3.74). This suggests that the marginal utility of additional data depends on the language family and its representation in the pretraining pool. Languages already well represented (e.g., Hindi) benefit more strongly from scaling, whereas typologically distant and rnder-represented languages (e.g., Tamil) remain challenging even with larger data sizes.
5.4.2 Effect of Model Components
We evaluate the contribution of each component using a controlled ablation with four settings trained on Set AUSet B and evaluated on Set A, Set B, and Set (i) a dual-stream bascline that retains both encoders and AFUM but replaces the LLM with 2-layer MLP head; (ii) Whisper LLM (semantic stream only); (iii) m-HuBERT LLM (acoustic stream only); and (iv) the full SAFARI-LLM with both streams unified by AFUM and processed by the LoRA-adapted LLM. This isolates the effect of the LLM from (a) dual-stream fusion and (b) AFUM.
Table 14 shows that adding an LLM to a single stream yields consistent but modest gains over the dual- stream MLP baseline, whereas the largest improvement occurs when both semantic and acoustic streams are fused by AFUM and integrated by the pretrained LLM (0.73/0.73/0.66 EER). This supports our hypothesis that complementary semantic+acousticc cues, integrated by pretrained multilingual sequence model with long-range attention, crucial for robust cross-fanin fross-family and zero-shot transfer (cf. Table 10).
6 Limitations and Future Work
While SAFARI-LLM advances multilingual deepfake detoction, several limitations remain. First, our dataset, IndicFake, is region-focused and primarily targets Indic languages. We position it complement to existing
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global resources rather than replacement and therefore refrain from claiming universal generalizability from IndicFake alone. To contextualize the scope, we also evaluate the proposed methodology on non-Indic benchmarks, WaveFake (English+ + Japanese) and DECRO (English + Chinese), so that model relevance is assessed beyond the Indic region (sce Table 6 and Table 7).
Second, the synthetic speech sources used in this work cover a few representative TTS architectures; Fx- panding to more diverse and stronger generators an important next step. Third, although SAFARI-LLM achieves strong cross-lingual transfer, performance remains challenging between typologically distant lan- guages (e.g., English Chinese), highlighting persistent language-specifte artifacts. Fourth, we do not explicitly evaluate robustness to partial manipulations or controlled woise/codee/chanen effects, though the diversity of YouTube-sourced real data provides some natural robustness. We view these limitations as opportunities for future work: expanding coverage to additional language families, incorporating more diverse synthesis pipelines (including stronger contemporary generators), ensuring fully speaker-disjoint test splits, extending the framework to partial-fake fake detoction and channel/codec robustness, and developing lighttr-weight. variants (e-g., time-series foundation models such as Moment, TimesFM, Granite TSPulse as alternatives to the LLM backbone presents a promising direction for roducing model size while retaining strong representational ca- pacity. Finally, the architecture integrates Whisper-large, m-HuBERT, and a 7B LLM backbone; despite parameter-efficinnt LoRA fine-tuning, this remains computationally heavier than several baselines.
for edge deployment. In addition, exploring modern
We view these limitations as opportunities for future work: expanding coverage to additional language famn- ilies, incorporating more diverse synthesis pipelines (including stronger contemporary generators), ensuring fully speaker-disjoint test splits, extending the framework to partial-fake detection and channel/code ro- bustness, and developing lighter-weight variants (e.g., distillation/ ccmppession) for edge deployment. In addition, exploring modern time-series foundation models such as Moment Goswami et al. (2024), TimesFM
distillation/ooppresiiolll
Das et al. (2024), or Granite TSPulse Anonymous (2025) as alternatives to the LLM backbone presents a promising direction for reducing model size while retaining strong ropresentatinnal capacity.
7 Conclusion
This work introduces two transformative contributions to multilingual deepfake detection: the IndicFake dataset and the SAFARI-LLM model. The IndicFake dataset, encompassing over 4.2 million audio samples across 18 Indian languages from the Indo-Faropean, Dravidian, and Sino-Tibelan families, establishes a new benchmark for linguistic diversity in deepfake research. IndicFake exhibits minimal overlap (Jaccard similarity ranging from 0.00 to 0.06) when compared individually to existing datasets, making it a robust resource for evaluating detection models across varied linguistic contexts. The proposed SAFARI-LLM, novel dual-stream architecture, seamlessly integrates Whisper's semantic embeddings and m- m-HuBERT's acoustic features through an adaptive Audio Feature Unification Module (AFUM). Enhanced by a LoRA-fine- tuned LLaMA-7B model, SAFARI-LLM achieves state-of-the-art performance, delivering superior accuracy, exceptionally low error rates, and robust generalization across diverse languages and synthesis methods. Comprehensive experiments on IndicFake, DECRO, and WaveFake datasets demonstrate SAFARI-LLM's ability to balance semantic and acoustic information, outperforming existing models in cross-lingual and cross-language family scenarios while maintaining stability across varied deepfake generation techniques.
These advancements set a new standard for multilingual deepfake detection, offering scalable and reliable solutions for real-world deployment. In the future, we aim to expand IndicFake to include additional low- resource languages, further broadening its applicability. Optimization efforts will focus on model compression and efficiont adaptation to enable deployment in resourco-constraneed environments. Additionally, integrat- ing phonetic-aware modeling and targeted artifact identification will enhance cross-lingual robustness, paving the way for universally effective audio deepfake detection systems.
References
Darius Afchar, Vincent Nozick, Junichi Yamagishi, and Isao Echizen. Mesonet: a compact facial video forgery detection network. In 2018 IEF. International Workshop on Information Forensics and Security
22

---

Published in Transactions on Machine Learning Research (11/2025)
(WIFS), pp. 1-7, 2018. doi:
Anonymous. TSPulsc: Tiny pro-trained models with disentanglod representations for rapid time-scries analysis. In Submitted to The Fourteenth International Conference on Learning Representations, 2025. under review.
URL http:/pppnrrrrieee..eetttrrrrr/r
Zhongjie Ba, Qing Wen, Peng Cheng, Yuwei Wang, Feng Lin, Li Lu, and Zhenguang Liu. Transferring audio doepfake detection capability across languages. In Proceedings of the ACM Web Conference 2023, pp. 2033 2044, 2023.
Marcoly Zanon Boito, Vivek Iyer, Nikolaos Lagos, Laurent Besacier, and Ioan Calapodescu. mhubert-147 A compact multilingual hubert model. CoRR, abs/ 0ass/2406.06377 2024. doi: 10.48550/ARRS ARRIVV 06371. URL https://doi http////oo.or/////// 48550/arXiv 2406 06371
Yujie Chen, Jiangyan Yi, Jun Xue, Chenglong Wang, Xiaohui Zhang, Shunbo Dong, Siding Zeng, Jianhua Tao, Zhao Lv, and Cunhang Fan. Rawbmamba: End-to-end bidirectional state space model for audio doepfake detcction. In INTERSPEECH. ISCA, 2024.
Abhimanyu Das, Weihao Kong, Rajat Sen, and Yichen Zhou. A decoder-only foundation model for time- serics forecasting. In Forty-first International Conference on Machine Learning, 2024.
Jinxiao Fan, Haolin Chu, Liang Liu, and Huadong Ma. Llmair: Adaptive reprogramming large language model for air quality prediction. In IEEE International Conference on Parallel and Distributed Systems (S),ADS), pp. 423 043-430,22 2024.
Joel Frank and Lea Schönherr. WaveFake: A Data Set to Facilitate Audio Deepfake Detection. In Thirty-fifth Conference on Neural Information Processing Systems Dalasets and Benchmarks Track, 2021.
Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai, Shuo Li, and Artur Dubrawski. Moment: A
family of open time-series foundation models. In International Conference on Machine Learning, 2024.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Ges- mundo, Mona Attariyan, and Sylvaín Gelly. Parametor-cfficiet transfer learning for NLP. In International Conference on Machine Learning, pp. 2790 2799. PMLR, 2019.
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXie preprint urxiv:2106. 09685, 2021.
Tahir Javed, Sumanth Doddapaneni, Abhigyan Raman, Kaushal Santosh Bhogale, Gowtham Ramesh, Anoop Kunchukuttan, Pratyush Kumar, and Mitesh M. Khapra. Towards building ASR systems for the next billion users. In AAAI, pp. 10813 10821. AAAI Press, 2022.
Zhoulin Ji, Chenhao Lin, Hang Wang, and Chao Shen. Speech-forensic Towards comprehensive synthetic speech dataset establishment and analysis. In IJCAI, pp. 413 421. ijeai.org, 2024.
Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, and Qingsong Wen. Time-llm: Time series forecasting by reprogramming large language models. In International Conference on Learning Representations, ICLR, 2024.
Jee-weon Jung, Hee-Soo Heo, Hemlata Tak, et al. AASIST: audio anti-spoofing using integrated spectro- temporal graph attention networks. In ICASSP, pp. 6371, 2022.
Piotr Kawa, Marcin Plata, and Piotr Syga. Specrnet: Towards faster and more accessible audio deepfake detection. In 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Commanications (TrustCom), pp. 792-799, 2022a. doi: 10.1109/ 10.1109/Trust TTuuSCComm66336222200001
Piotr Kawa, Marcin Plata, and Piotr Syga. Attack agnostic datasct: Towards generalization and stabilization of audio deepfake detection. In Interspeech, 2022b. doi: 10.21437 /It44///nntrsppccc.ooo 2002- URL https:
1007
7/doi. 1tdbiioorg/1..214333 21437 htt.44/////ttrrpechh.h.m 1000 2002-10078
23

---

Published in Transactions on Machine Learning Research (11/2025)
Piotr Kawa, Marcin Plata, Michal Czuba, Piotr Szymański, and Piotr Syga. Improved DeepFake Detection Using Whisper Features. In Proc. INTERSPEECH 2023, pp. 4009-4013, 2023. doi: 10.21437/ 1114///nnnespeech. 2023-1537.
Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis. Advonces in Neural Information Processing Systems, 33:17022-17033, 2020.
Pavel Korshunov and Sébastien Marcel. Cross-database evaluation of audio-based spoofing detection systems. Nelson Morgan (ed.), 17th Annual Conference of the International Speech Communication Association, Interspeech 2016, San Francisco, CA, USA, Seplember 8-12, 2016, pp. 1705 1709. ISCA, 2016. doi: 2016-13330 1326
10/21487INIIERSSPIICI 2016. 1326.
URL https:///oo...... /trgg/10..0 10. 21137
Gokul Karthik Kumar, V PraveenS., Pratyush Kumar, Mitesh M. Khapra, and Karthik Nandakumar.
Towards building text-to-speech systems for the next billion users. ICASSP 2023 2023 IEEE In- ternational Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, 2022. URL
http:///ppp.smmmnnisscc.oooooooe wtt.:/eccccaaaaooorrrrorrrpopeee ort.oorpg//D 22:3:pss 253581696
Xinfeng Li, Kai Li, Yifan Zheng, Chen Yan, Xiaoyu Ji, and Wenyuan Xu. Safeear: Content pireecy-preservng audio deepfake detection. In Proceedings of the 2024 on ACM SIGSAC Conference on Computer and Communiccations Securily, pp. 3585 3599, 2024.
Haoxin Ma, Jiangyan Yi, Chenglong Wang, Xinrui Yan, Jianhua Tao, Tao Wang, Shiming Wang, Le Xu, and Ruibo Fu. Fad: A chinese dataset for fake audio detection. arXiv preprint arXiv:2201.1230 2022.
Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, and De- vleena Das. Reprogramming pretrained language models for antibody sequence infilling. In International Conference on Machine Learning, ICML, volume 202 of Proceedings of Machine Learning Research, pp. 24398 24419, 2023.
Nicolas M Müller, Pavel Czempin, Franziska Dieckmann, Adam Froghyar, and Konstantin Böttinger. Does audio deepfake detection generalize? arXiv preprint arXiv:2903.66263 2022.
Nicolas M Müller, Piotr Kawa, Wei Herng Choong, et al. Mlaad: The multi-languan audio anti-spoofing dataset. International Joint Conference on Neural Networks (IJCNN), 2024.
Vineel Pratap, Andros Tjandra, Bowen Shi, et al. Scaling speech technology to 1, 000+ languages. J. Mach. Learn. REs., 25:97:1-97:52, 2024.
Joan Puigeerver, Carlos Riquelme, Basil Mustafa, and Neil Houlsby. From sparse to soft mixtures of experts. arXiv preprint Xic:2303 00951, 2023.
Alec Radford, Jong Wook Kim, Tao Xu, Greg Brockman, Christine McLeavey, and Ilya Sutskever. Robust speech recognition via large-scale weak supervision, 2022. URL htttpe:/aaxiw..rr//ab..t. 2212. 04356
Rishabh Ranjan, Mayank Vatsa, and Richa Singh. Uncovering the deceptions: An analysis on audio spoofing detection and future prospects. In Proceedings of the Phirty-Second International Joint Conference on Artificial Intelligence, pp. 6750-6758, 2023.
Rishabh Ranjan, Bikash Dutta, Mayank Vatsa, and Richa Singh. Faking fluent: of multilingual deepfake detection. In 2024 IEEE International Joint Conference on Biometrics (IJCB), pp. 1-10, 2024. doi: 10.1109/1JCB621 TE..C666174..2......
Unveiling the achilles' heel
Chandan KA Reddy, Vishak Gopal, and Ross Cutler. Dnsmos: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors. In ICASSP 2021-2021 IEEE International Conference Acoustics, Speech and Signal Processing (ICASSP), pp. 6493-6497. IEEE, 2021.
Chandan KA Reddy, Vishak Gopal, and Ross Cutler. Dnsmos p. 835: A non-intrusive perceptual objective speech quality metric to evaluate noise suppressors. In ICASSP 2022-2022 IEEE international conference on acousties, speech and signal processing (ICASSP), pp. 886 890. IEFE, 2022.
24

---

Published in Transactions on Machine Learning Research (11/2025)
Ricardo Reimao and Vassilios Tzerpos. For: A dataset for synthetic speech detection. In 2019 International Conference on Speech Technology and Human- Comann-omppuer Dialogue (SpeD); pp. 1-10. IEFE, 2019.
Yi Ren, Yangjun Ruan, Xu Tan, Tao Qin, Sheng Zhao, Zhou Zhao, and Tie-Yan Liu. Fastspeech: Fast, robust and controllable text to speech. Advances in Neural Information Processing Systems, 32, 2019.
Yi Ren, Chenxu Hu, Xu Tan, Tao Qin, Shong Zhao, Zhou Zhao, and Tic-Yan Liu. Fastspeech 2: Fast and high-quality end-to-end lext to speech. ar preprinl arXiv:2006. 04558, 2020.
Eros Rosello, Alejandro Gomez-Alanis, Angel M. Gomez, and Antonio Peinado. A conformer-based classifier for variable-length utterance processing in anti-spoofing. In Proc. INTERSPEECH 2029, pp. 5281-5285, 2023. doi: 10.21437/ 1 Interspeech. wawpech...22-1110 2020-1820.
Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans, and Authony Larcher. End-to-end anti-spoofing with rawnet2. In ICASSP 2021-2021 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), pp. 6369 6373. IEEE, 2021a.
Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas W. D. Evans, and Anthony Larcher. End-to-end anti-spoofing with rawnet2. In IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6369 6373, 2021b.
Hemlata Tak, Jee weon Jung, Jose Patino, Madhu Kamble, Massimiliano Todisco, and Nicholas Evans. End-to-end spectro-temporal graph attention networks for speaker verification anti-spoofing and speech deepfake detection. In Proc. 2021 Edition of the Automatic Speaker Verification and Spoofing Counter- measures Challenge, pp. 1-8, 2021c. doi: AAA.//////SSSOOO.2222.2. 20021-1.
Hemlata Tak, Massimiliano Todisco, Xin Wang, Jee weon Jung, Junichi Yamagishi, and Nicholas W. D. Evans. Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data aug- mentation. ArXiv, fbs//220..1222 2022.
Pablo Andrés Tamayo Flórez, Rubén Manrique, and Bernardo Pereira Nunes. HABLA: Dataset of Latin American Spanish Accents for Voice Anti-spo.fing. In Proc. INTERSPEECH 2023, pp. 1963-1966 2023. doi: 10.21437 http///eeepecch222---2-
Kartik Thakral, Rishabh Ranjan, Akanksha Singh, Akshat Jain, Richa Singh, and Mayank Vatsa. ILLU- SION: Unveiling truth with a comprehensive multi-modal, multi-lingual deepfake dataset. In The Thir- leenth International Conference on Learning Represenlations, 2025. URL
forum?id=qn1G32pm
Xiaohai Tian, Zhizheng Wu, Xiong Xiao, Eng Siong Chng, and Haizhou Li. Spoofing detection under noisy conditions: preliminary investigation and an initial database. arXiv preprint arXiv:1602. 02950, 2016.
Hugo Touvron, Louis Martin, Kevin Stone, et al. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXto:2307.09288, 2023.
Vicuna. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. httpss//vicumm Imsys.org/ 2023.
Xin Wang, Junichi Yamagishi, Massimiliano Todisco, et al. Asvspoof 2019: A large-scale public database of synthesized, converted and replayed speech. Computer Speech & Language, 64:101114, 2020.
Xin Wang, Héctor Delgado, and Hemlataothers Tak. Asvspoof 5: Crowdsourced speech data, deepfakes, and adversarial attacks at scale. arXiv preprint arXiv:2408.0877 2024.
Jee weon Jung, Hee-Soo Heo, Hemlata Tak, Hye jin Shim, Joon Son Chung, Bong-Jin Lee, Ha jin Yu, and Nicholas W. D. Evans. Aasist: Audio anti-spoofing using integrated spectro-temporal graph attention networks. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6367-6371, 2022a.
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Published in Transactions on Machine Learning Research (11/2025)
Jee weon Jung, Youjin Kim, Hee-Soo Heo, Bong-Jin Lee, Youngki Kwon, and Joon Son Chung. Pushing the limits of raw waveform speaker recognition. In Proc. Interspeech 2022, pp. 2228 2232, 2022b. doi: 10.21437/Iniee 00.24437/Itersppeech.22 /ttp:/erreecch22222222222
Xiang Wu, Ran He, Zhenan Sun, and Tieniu Tan. A light for deep face representation with noisy labels. IEEE Transactions Information Forensics and Security, 13(11):2884-288) 2018. doi: 10.1109/TIFS.
2018.2833032.
Zhizheng Wu, Tomi Kinnunen, Nicholas Evans, Junichi Yamagishi, Cemal Hanilçi, Md Sahidullah, and Aleksandr Sizov. Asvspool 2015: the first automatic speaker verification spooling and countermeasures challenge. In Sirtecnth annual conference of the international speech communication association, 2015.
Xiong Xiao, Xiaohai Tian, Steven Du, et al. Spoofing speech detection using high dimensional magnitude and phase features: the NTU approach for asvspoof 2015 challenge. In INTERSPEECH, pp. 2052-2056. ISCA, 2015.
Junichi Yamagishi, Xin Wang, Massimiliano Todisco, et al. Asvspoof 2021: accelerating progress in spoofed and deepfake speech detection. In VSVpooof 2021 WorkshoopAAuommatie Speaker Verification and Spoofing Coutermeasures Challenge, 2021.
Ziwei Yan, Yanjie Zhao, and Haoyu Wang. Voicewukong: Benchmarking deepfake voice detection. arXiv preprint Kiv::2240 06348, 2024.
Jiangyan Yi, Ruibo Fu, Jianhua. Tao, et al. ADD 2022: the first andio deep synthesis detection challenge. In ICASSP, pp. 9216 9220. IEEE, 2022.
Jiangyan Yi, Jianhua Tao, Ruibo Fu, et al. Add 2023: the second audio deepfake detection challenge. DADA @IJCAI, 2023. URL https: pettss:/app5s //api. semanticscholar. wwwpaoolla.rrro/oooooo/eeoooe ::t/CorpusID::233500 2588441572
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Reviews:
[{"review_id": "5Dg5uddQBX", "reviewer": "Reviewer_JGQc", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "#### Strengths\n- The paper identifies an important gap in deepfake identification research, namely cross-lingual and low-resource language generalization.\n\n- The proposed dataset is potentially valuable.\n\n#### Weaknesses\n- YouTube as a baseline source is questionable since the platform contains loads of AI-generated content these days. How do we know the real data is real?\n\n- I did not understand why the LLM is part of the proposed SAFARI-LLM architecture. The paper claims it leverages advanced semantic reasoning, but why would we expect deepfake speech to differ semantically from standard speech? I believe this assumption requires an ablation to be convincing.\n\n- Does the experimental setup in Table 6 follow the protocol of the wavefake paper? This is important to ensure we can compare the results.\n    - The original wavefake setup emphasizes generalization to unknown generators, its important not to loose this aspect when considering additional aspects like linguistic generalization.\n\n\n#### Code:\n- I have tried to access the code, yet the repository link has expired.\n\n\n#### Other:\n- I am not sure TMLR is the right venue for this paper. It might have been worthwhile to review this work at DMLR, which is single-blind and allows reviewers to access the data. The data is a key component of this work, yet I have not been able to review it.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The IndicFake paper presents a new dataset that contains many languages spoken in modern India. In addition to the dataset, a SAFARI-LLM detection architecture is presented. The proposed dataset addresses a crucial gap in the deepfake detection literature by adding rich data covering widely spoken languages from India. Both real and synthetic samples are provided. The experimental section introduces the SAFARI-LLM for deepfake detection on audio signals.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "- I did not understand why the model is called a large language model (LLM) even though it is not a text, but a speech model. Please consider explaining the name in light of the related work on text or renaming the model.\n\t- A possible explanation could be built on ablating the LLM weights. Do they help in this context?\n- Is it possible to use the same font in Figure 2 and the rest of the paper?\n- Figure 3 is hard to read. Is it possible to increase the font size?\n- Are the plots in Figure 5 inspired by the Wavefake paper ( https://arxiv.org/pdf/2111.02813 , figure 12)? If yes, please add a citation.\n- If the experiments in Table 6 did not follow the train/test split from the original wavefake paper, an additional experiment should be added that respects the original setup, and therefore produces comparable numbers.\n- The paper proposes a dual-stream architecture, but never evaluates the two streams individually. To be acceptable, an additional ablation study is required to demonstrate the merits of all model parts by presenting concrete evidence. The evidence should additionally justify LLM part of the architecture. In its current form, the paper does not present sufficient evidence for both claims.", "broader_impact_concerns": "None, the paper aims to mitigate potential damages caused by generative machine learning.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "WcHuvTVTrM", "reviewer": "Reviewer_dSQ7", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Pros:\n\n+ IndicFake is the largest and most linguistically diverse multilingual audio deepfake dataset to date.\n\n+ A new SAFARI-LLM model uses semantic features from Whisper and acoustic features from m-HuBERT, merges them with an adaptive fusion module, and then applies a fine-tuned LLaMA-7B to detect deepfakes.\n\n+ Extensive experiments are provided.\n\nCons:\n\n+ While strong for Indian languages, the dataset is heavily region-focused and does not cover many global languages.\n\n+ The computation cost of the model seems high. SAFARI-LLM requires Whisper-large, m-HuBERT, and LLaMA-7B.\n\n+ An ablation study is needed to evaluate the individual contributions of Whisper-large and m-HuBERT.\n \n+ EER is still relatively high in some cross-lingual scenarios (e.g., English→Chinese). Does it suggest that language-specific artifacts remain an issue?\n\n+ While the proposed SAFARI-LLM achieves consistent and often leading results across multiple datasets and evaluation settings, the performance gains over strong baselines appear limited in many cases. For example, in within-language detection, accuracy is already very high for existing models (often >99%), leaving minimal room for improvement. The gains in these scenarios are therefore marginal. \n\n+ Given the marginal improvements, it’s unclear if such a large, LLM-based model is necessary.\n\n+ The authors did not provide audio samples.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "In this paper, the authors introduce IndicFake, a 4.2M-sample multilingual audio deepfake dataset covering English and 17 Indian languages, and SAFARI-LLM, a new dual-stream semantic–acoustic detection model. SAFARI-LLM fuses Whisper semantic embeddings and m-HuBERT acoustic features via an adaptive module, then classifies with a LoRA-finetuned LLaMA-7B. Experiments on IndicFake, DECRO, and WaveFake show state-of-the-art cross-lingual and cross-language family generalization.", "claims_and_evidence_yn": "Yes", "claims_explanation": "", "audience_yn": "Yes", "audience_explanation": "", "requested_changes": "Please address the raised concerns:\n\n+ While strong for Indian languages, the dataset is heavily region-focused and does not cover many global languages.\n\n+ The computation cost of the model seems high. SAFARI-LLM requires Whisper-large, m-HuBERT, and LLaMA-7B.\n\n+ An ablation study is needed to evaluate the individual contributions of Whisper-large and m-HuBERT.\n \n+ EER is still relatively high in some cross-lingual scenarios (e.g., English→Chinese). Does it suggest that language-specific artifacts remain an issue?\n\n+ While the proposed SAFARI-LLM achieves consistent and often leading results across multiple datasets and evaluation settings, the performance gains over strong baselines appear limited in many cases. For example, in within-language detection, accuracy is already very high for existing models (often >99%), leaving minimal room for improvement. The gains in these scenarios are therefore marginal. \n\n+ Given the marginal improvements, it’s unclear if such a large, LLM-based model is necessary.\n\n+ The authors did not provide audio samples.", "broader_impact_concerns": "The authors are suggested to add a section to discuss broader impact of the work.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "a9TXc88bt0", "reviewer": "Reviewer_LHEZ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "Strengths\n1. A medium scale (7.3K hours) dataset including multiple languages and languages from multiple language families, which enable studies on cross-lingual & cross-language family transfer performance\n2. Carefully balanced distribution (sampling based on representativeness, gender, duration, and topic distribution)\n3. Good empirical numbers on the tested datasets (IndicFake, DECRO, WaveFake)\n\nWeakness\n1. The proposed dataset is extremely limited in terms of TTS sources. It only sources from 4 datasets from 3 rather outdated methods that have known artifacts (HiFi-GAN, VITS, FastSpeech2). These models generate speech with limited diversity. Models trained on this dataset is likely recognizing the artifacts of specific models, rather than learning to detect general fake audio samples. To validate the general effectiveness (rather than \"detecting fake samples from a specific model\"), the authors should test the detection performance on held out TTS samples that is generated with unseen TTS with very different architectures / characteristics.\n2. More in-depth ablation should be added to provide additional insights about gain of adding more data in one language versus gain of adding same amount of data in another language of the same family or different family\n3. There is not controlled ablation studies on the effectiveness of the model. Specifically, how effective is adding mHuBRT and Whisper feature respectively\n4. Additional dimensions, like noise robustness, partially fake samples are not studied. The paper should also evaluate on additional datasets like ASVspoof 2021\n5.", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper studies fake speech detection and made two main contributions\n1. Creating a multilingual / multi-language family Indian-language focused dataset\n2. Develop SAFARI-LLM that fine-tunes an LLM with two speech features (Whisper-based and HuBERT-based)\n\nEmpirical results demonstrated better performance of the proposed model compared to baseline on in-domain and cross-language transfer.", "claims_and_evidence_yn": "No", "claims_explanation": "", "audience_yn": "No", "audience_explanation": "", "requested_changes": "See the weaknesses aboec", "broader_impact_concerns": "No", "additional_comments": "", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)