Speech Foundation Model Ensembles for the Controlled Singing Voice Deepfake Detection (CtrSVDD) Challenge 2024
Anmol Guragain Tianchi Liu Zihan Pan Hardik B. Sailor Qiongqiong Wang



A Lesion-aware Edge-based Graph Neural Network for Predicting Language Ability in Patients with Post-stroke Aphasia
    Zijian Chen1, Maria Varkanitsa2, Prakash Ishwar1, Janusz Konrad1, 
 Margrit Betke3, Swathi Kiran2 Archana Venkataraman1
    September 9, 2024 – Version 1.0
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§ ABSTRACT 
This work details our approach to achieving a leading system with a 1.79% pooled equal error rate (EER) on the evaluation set of the Controlled Singing Voice Deepfake Detection (CtrSVDD). The rapid advancement of generative AI models presents significant challenges for detecting AI-generated deepfake singing voices, attracting increased research attention. The Singing Voice Deepfake Detection (SVDD) Challenge 2024 aims to address this complex task. In this work, we explore the ensemble methods, utilizing speech foundation models to develop robust singing voice anti-spoofing systems. We also introduce a novel Squeeze-and-Excitation Aggregation (SEA) method, which efficiently and effectively integrates representation features from the speech foundation models, surpassing the performance of our other individual systems. Evaluation results confirm the efficacy of our approach in detecting deepfake singing voices. 
The codes can be accessed at <https://github.com/Anmol2059/SVDD2024>.








Singing voice, deepfake detection, anti-spoofing, SVDD, SSL, SEA




§ INTRODUCTION


With the rapid development of generative AI technology, the quality of audio synthesis has significantly improved, making it increasingly difficult to distinguish between bona fide and spoofed audio. However, this progress also poses significant risks to human voice biometrics and can deceive both automatic speaker verification systems and their users <cit.>. Additionally, the proliferation of spoofed speech presents a serious threat to cybersecurity, as it can be used to manipulate information, conduct fraud, and bypass security measures that rely on voice authentication. Finding effective ways to detect spoofing attacks and protect users from the threat of spoofed speech is becoming increasingly important. Therefore, speech anti-spoofing, also known as speech deepfake detection, has emerged <cit.>. It is dedicated to developing reliable automatic spoofing countermeasures (CMs), which is of utmost importance to society and the ethical applications of generative models.


Unlike speech spoofing, creating deepfakes of singing voices introduces distinct challenges. This complexity arises from the inherently musical aspects of singing, such as varying pitch, tempo, and emotion, as well as the frequent presence of loud and intricate background music <cit.>. These factors make it more difficult to detect deepfakes in singing compared to regular speech, which typically features a more consistent and predictable sound pattern.
Recently, the speech anti-spoofing research community has been increasingly focusing on this challenging issue, resulting in the development of related datasets <cit.>, challenges <cit.>, and models <cit.>.
The Singing Voice Deepfake Detection (SVDD) Challenge 2024 aims to address these challenges by fostering the development of robust detection systems <cit.>. 

Speech foundation models are large, pre-trained models designed to serve as the backbone for various speech-related tasks, including speaker verification, speech recognition, and more <cit.>. Many of these models rely on self-supervised learning (SSL) to develop robust speech representations, such as WavLM <cit.> and wav2vec2 <cit.>. These models excel in learning high-quality representations that can be fine-tuned for specific downstream tasks. Recently, many studies on speech anti-spoofing have adopted this approach and achieved state-of-the-art performance <cit.>. The progress of these studies and their promising performance motivate us to continue exploring along this particular line.

This work details our participation in the CtrSVDD track of the SVDD Challenge 2024. 
We detect singing voice deepfakes by ensembling models developed using speech foundation models, data augmentation techniques, and various layer aggregation methods. Specifically,  the default Weighted Sum aggregation method fixes weights after training, limiting adaptability to new data. The recently proposed Attentive Merging (AttM) method <cit.>, while powerful, can lead to overfitting on small datasets. To address these issues, inspired by Squeeze-and-Excitation Networks (SENet) <cit.>, we propose the SE Aggregation (SEA) method. This method dynamically assigns weights and mitigates overfitting issues, enabling our best individual model to achieve an EER of 2.70% on the CtrSVDD evaluation set. Further investigations show that ensembling systems enhances robustness and performance, achieving our best result of 1.79% EER.










§ METHODOLOGY





 §.§ Data Augmentation



We employ the RawBoost augmentation <cit.>, which introduces various types of noise to the audio data to simulate real-world acoustic variations. These augmentation types include:



  * (1) Linear and non-linear convolutive noise (LnLconvolutive noise). This involves applying a convolutive distortion to the feature set by filtering the input signal with notch filter coefficients, iterating N_f times,, and raising the signal to higher powers to simulate real-world distortions.


  * (2) Impulsive signal-dependent noise (ISD additive noise). This is introduced by adding noise to a random percentage of the signal points, scaled by the original signal's amplitude.


  * (3) Stationary signal independent noise (SSI additive noise). This represents stationary signal-independent noise, which is added uniformly across the signal.










  §.§.§ Parallel Noise Addition


We adopt a parallel noise addition strategy to independently incorporate multiple noise characteristics. We process the input feature through both LnL Convolutive Noise and ISD Additive Noise algorithms simultaneously, resulting in two separate noisy signals. These signals are then combined by summing and normalizing to maintain consistent amplitude levels. This parallel approach allows each noise type to influence the signal independently, effectively capturing the combined effects of convolutive and impulsive noise, and providing a robust simulation of complex noise conditions. This method is referred to as the `parallel: (1)+(2)' approach described in RawBoost <cit.>.



  §.§.§ Sequential Noise Addition

We use a sequential noise addition process to enhance the robustness of our features, incorporating the aforementioned three types of noise. This sequential approach ensures comprehensive noise simulation and results in various combinations such as `series: (1)+(2)', `series: (1)+(3)', and `series: (2)+(3)', following those in RawBoost <cit.>.








 §.§ Individual Models Description



  §.§.§ Frontend


In this subsection, we provide a detailed overview of the frontends used in our individual models, emphasizing their ability to efficiently process raw audio data.

Raw waveform. Following the baseline system described in the SVDD challenge 2024 <cit.>, we employ RawNet2 <cit.>-style learnable SincConv layers with 70 filters as the frontend. These SincConv layers are designed to effectively capture essential features from raw audio signals, enhancing the model's ability to process and analyze audio data for subsequent tasks.

wav2vec2.
The wav2vec2 model offers significant advantages in effectively capturing a wide range of audio features directly from raw audio inputs <cit.>. This model excels in extracting detailed and nuanced information from audio data, which can then be utilized for various downstream tasks such as speaker verification, speech recognition, and speech anti-spoofing. By processing the raw audio waveforms without requiring extensive pre-processing, wav2vec2 enhances the ability to perform complex audio-related tasks with improved accuracy and efficiency. This direct approach not only simplifies the workflow but also improves the overall performance of the subsequent processing and classification tasks <cit.>.

WavLM.
The WavLM <cit.> is a large-scale pre-trained speech foundation model for addressing the multifaceted nature of speech signals, including speaker identity, paralinguistics, and spoken content. Its robust performance on the SUPERB benchmark <cit.> underscores its potential versatility across diverse speech processing applications. Given its advanced capabilities in modeling and understanding complex speech patterns, WavLM holds promise for use in specialized area of singing voice deepfake detection. The model's ability to capture intricate vocal nuances and sequence ordering could be instrumental in identifying synthetic patterns in singing voices, thereby contributing to the SVDD task.





  §.§.§ Layer Aggregation Strategy

The layer aggregation strategy in speech foundation models refers to the technique of combining information from multiple layers to enhance the model's performance in speech-related downstream tasks like speaker verification, emotion recognition, and anti-spoofing. Each layer in a speech foundation model captures distinct aspects and features of the input waveform. By aggregating these layers, the model can leverage a richer set of features, combining low-level acoustic information from early layers with higher-level semantic and contextual information from later layers. This process typically involves techniques such as concatenation, weighted sum, or attention mechanisms to effectively aggregate the multi-layer representations <cit.>. These learned weights allow the model to emphasize more relevant features and reduce noise or less important information. In this work, we explore weighted sum and attentive merging (AttM) <cit.>. Inspired by SE <cit.>, we propose SE Aggregation. These three methods are illustrated in Fig. <ref>, and the details are as follows:

Weighted Sum. The weighted sum method combines outputs from multiple neural network layers using adjustable parameters. Each layer's output receives a unique weight, enabling the model to determine the optimal contribution of each layer to the final representation. These weights are adjusted during the training process to enhance the model's performance and remain fixed during inference.


Attentive Merging (AttM). The AttM <cit.> approach emphasizes the most relevant features for anti-spoofing by averaging the embeddings across the time dimension and applying a fully connected layer to squeeze the hidden dimensions. Attentive weights are computed using a sigmoid activation function, which are then applied to the stack of embeddings. Finally, a linear projection network merges these re-weighted embeddings, retaining global spatial-temporal information while emphasizing the most relevant transformer layers for anti-spoofing. This method not only achieves state-of-the-art performance but also improves computational efficiency by utilizing only a subset of the transformer layers <cit.>.

Proposed SE Aggregation (SEA). The weighted sum method is simple yet effective. However, its weights are fixed after training, limiting its adaptability to new data. The AttM method, though powerful, requires a large number of parameters, which can lead to overfitting on small datasets. 
Most of these parameters are concentrated in the final linear layer.
To address this, we introduce a new method called SE Aggregation (SEA), inspired by SENet <cit.>, which eliminates the need for the final linear layer. SEA enables a lightweight, cross-layer attention-based aggregation.

The SE module is well-knwon for its ability to adaptively recalibrate channel-wise feature responses by explicitly modeling interdependencies between channels <cit.>. This recalibration enhances the representational capability of the network by focusing on the most informative features and suppressing less useful ones, which is crucial for tasks requiring high precision and robustness <cit.>. This method has been widely applied and validated in speech tasks, such as anti-spoofing <cit.> and speaker verification <cit.>. Instead of using this approach to re-weight channels, we employ it to compute layer attention, dynamically emphasizing important channels for each sample. The proposed SEA method operates by initially compressing temporal and channel information through a global average pooling (GAP) operation, creating a layer-wise descriptor. This descriptor is then used to selectively emphasize informative features, as illustrated in Fig. <ref> (c).



Notably, the layer aggregation technique is only applied to the speech foundation model-based systems in this work. The RawNet2-based system does not require the layer aggregation strategy.



  §.§.§ Backend




The audio anti-spoofing using integrated spectro-temporal graph attention networks (AASIST) functions as the  model, leveraging graph-based attention mechanisms to capture spectral and temporal audio features <cit.>. It includes several key components <cit.>:

    
  * The Graph Attention Layer (GAT) computes attention maps between nodes and projects them using attention mechanisms. This layer consists of linear layers, batch normalization, dropout, and Scaled Exponential Linear Unit (SELU) activation. Separate GAT layers are used for spectral and temporal  features.
    
  * The Heterogeneous Graph Attention Layer (HtrgGAT) processes both spectral and temporal feature nodes. It projects each type of node, generates attention maps, and updates a master node that represents the aggregated features. Sequential layers are used to refine these features further. 
    
  * The graph pooling layer reduces the number of nodes by selecting the top-k nodes based on attention scores. This process uses sigmoid activation and linear projection to compute the scores, with separate pooling layers for spectral and temporal features. 
    
  * The residual blocks apply convolutional layers, batch normalization, and SELU activation, similar to ResNet blocks, within the encoder to process input features. 
    
  * The attention mechanism derives spectral and temporal features from the encoded features, incorporating convolutional layers and SELU activation.











  §.§.§ Classifier

The classifier outputs the final predictions by utilizing the refined features extracted from the backend model, subsequently performing the classification task. In this work, the input comprises a concatenation of maximum and average temporal features, maximum and average spectral features, and master node features from the ASSIST backend. To enhance generalization, dropout is applied to this concatenated feature vector. The output is generated through a linear layer, which produces logits, representing the raw scores.



 §.§ Model Ensembling 


Model ensembling is a strategy where multiple models are combined to improve the overall performance and robustness of predictions. The rationale behind this approach is that different models may capture various aspects of the data, and combining them can result in better generalization on unseen data. This method is widely adopted in many works in the anti-spoofing task <cit.>. In this work, we ensemble the individual models by averaging their output scores.





§ EXPERIMENTAL SETUP




 §.§ Data Set





We utilized the official training and development datasets provided for the CtrSVDD track, available at Zenodo[<https://zenodo.org/records/10467648>]. Additionally, we incorporated other public datasets including JVS <cit.>, Kiritan <cit.>, Ofutan-P[<https://sites.google.com/view/oftn-utagoedb>], and Oniku[<https://onikuru.info/db-download/>] following the guidelines and scripts provided by the challenge organizers <cit.>. The combined dataset included a diverse range of singing voice recordings, both authentic and deepfake, segmented and processed[<https://github.com/SVDDChallenge/CtrSVDD_Utils>] to ensure consistency in training and evaluation. The details of the dataset partitions, along with the evaluation set statistics from <cit.>, are provided in Table <ref>.





 §.§ Training Strategy


We use the equal error rate (EER) as the evaluation metric. To ensure reproducibility, we consistently apply a fixed random seed of 42 across all systems. Our training process employs the AdamW optimizer with a batch size of 48, an initial learning rate of 1 × 10^-6, and a weight decay of 1 × 10^-4. The learning rate is scheduled using cosine annealing with a cycle to a minimum of 1 × 10^-9. For the loss function, we utilize a binary focal loss, a generalized form of binary cross-entropy loss, with a focusing parameter (γ) of 2 and a positive example weight (α) of 0.25.  To standardize input length, each sample is randomly cropped or padded to 4 seconds during the training. Our model is trained for 30 epochs, and the model checkpoint with the lowest EER on the validation set is selected for evaluation. 
All experiments are performed on a single NVIDIA A100 GPU.

For certain experiments marked in Table <ref>, we employ the Rawboost data augmentation strategy as introduced in Section <ref>. 
The RawBoost augmentation is sourced from the official implementation[<https://github.com/TakHemlata/SSL_Anti-spoofing>] and  follows the default settings <cit.>. Our utilization of wav2vec2 also references this implementation. The wav2vec2 <cit.> model used in this work is the cross-lingual speech representations (XLSR) model[<https://github.com/facebookresearch/fairseq/tree/main/examples/wav2vec/xlsr>]. The implementation of WavLM is derived from S3PRL[<https://github.com/s3prl/s3prl>].


















§ RESULTS




 §.§ Baselines


The organizers of the CtrSVDD Challenge 2024 provide two baseline systems, referred to as B01 and B02 in Table <ref> <cit.>.  B01, based on linear frequency cepstral coefficients (LFCCs), achieved a pooled EER of 11.37%, while B02, based on raw waveform, achieved a pooled EER of 10.39%. We re-implement B02 and obtain an improved performance of 9.45%, slightly better than the official implementation.




 §.§ Frontend


As indicated  in Table <ref>, when comparing wav2vec2-based models to WavLM-based models with the same type of augmentation (M2 vs. M4 for RawBoost `series: (1)+(2)', and M3 vs. M5 for  `parallel: (1)+(2)'), we observe that the WavLM-based models consistently perform better. Therefore, in this work, we focus more on experimenting with WavLM-based models.



 §.§ Data Augmentation


By comparing the wav2vec2-based models trained with and without `parallel: (1)+(2)' RawBoost augmentation <cit.> (M1 vs. M3), we observe a significant improvement in performance when the augmentation is applied. Further analysis based on various models and layer aggregation techniques reveals that the `parallel: (1)+(2)' configuration consistently provides better results compared to the `series: (1)+(2)' configuration (M2 vs. M3, M4 vs. M5, M6 vs. M7, M8 vs. M9), with an average relative performance improvement of 26.7%.
On the other hand, our experiments show that using type (3) of RawBoost (SSI additive noise) <cit.> does not yield more benefits (M11 and M12). Overall, RawBoost generally enhances system performance on the CtrSVDD dataset. Notably, benefiting from `parallel: (1)+(2)', the WavLM-based model with our proposed SEA (M9) achieves the best individual performance on the evaluation set, as shown in Table <ref>.







[8]We report the overall system performance according to the settings in the SVDD Challenge 2024 <cit.>, which calculates the pooled Equal Error Rate (EER) for attack types A09 to A13, excluding A14. Additionally, for the benefit of interested readers, we also include the pooled EER results for all attack types (A09 to A14).




































 §.§ Layer Aggregation Strategies

As shown in Table <ref>,when comparing different layer aggregation methods, we observe that the AttM strategy performs similarly to the weighted sum method in terms of pooled EER. Additionally, the AttM model (M7) achieves the best performance in the most sub-trials.
In this work, we simply utilize all WavLM layers, while the strength of AttM method lies in using fewer encoder layers. This not only lowers inference costs but also boosts performance <cit.>. This aspect is valuable for exploring in the SVDD task. 

Given that the weighted sum method lacks a cross-layer attention mechanism, which may limit the representation features extracted by the speech foundation model in complex musical scenarios, and that AttM's higher number of training parameters could lead to overfitting on small datasets, we propose the SEA method. 
Our proposed SEA aggregation method, based on the WavLM model, consistently outperforms both the Weighted Sum and AttM across different RawBoost augmentation scenarios, achieving an average relative reduction in EER by 16.7% and 19.1%, respectively.
With this proposed SEA, we achieve the best individual model performance of 2.70%, validating its superior performance and suitability for the task of singing voice deepfake detection.





 §.§ Model Ensembling
 




We explore ensembling models to enhance robustness and performance. The ensembled models and their corresponding evaluation EER are shown in Table <ref>.  Specifically,
we explore the model ensembling strategy by initially ensembling the top 5 individual models based on their performance on A09-A14 pooled EER. The E1 system, composed of M5, M7, M8, M9, and M10, achieves a 2.50% EER, outperforming all individual systems. Further investigation includes incorporating a wav2vec2-based model to enhance system diversity and robustness improvement. Consequently, we include the best wav2vec2 system, M3, and remove the weakest individual model, M8, from E1, resulting in E3, which performs at 2.13%. During post-evaluation, we further improve the ensemble performance by adding M2 and removing M5, achieving the best performance of 1.79%.


We note that although the pooled EER of the M2 model is not as good as other models in Table 2, it significantly contributes to ensemble performance. Since the evaluation labels have not yet been released, further analysis is not possible in this study. However, future investigations will help in understanding this improvement.

In Fig. <ref>, we provide a detailed comparison of the best individual model, the WavLM-based model with our proposed SEA (M9), and the best ensemble system (E5). The radar chart clearly illustrates that E5 consistently outperforms M9 in every sub-trial.

This demonstrates the superiority and robustness of ensemble systems by combining the strengths of multiple models, reducing the impact of individual model errors, and increasing overall prediction accuracy.






§ CONCLUSION






In this work, we present ensembled systems utilizing speech foundation models, demonstrating  significant promise in the task of singing voice deepfake detection (SVDD). 
Our novel layer aggregation strategy, SE Aggregation (SEA), enables the WavLM-based model to achieve the best performance with a 2.70% EER on the CtrSVDD evaluation set, outperforming all individual models. By implementing data augmentation techniques, such as RawBoost, our ensembled system further achieves a remarkable 1.79% pooled EER on the CtrSVDD evaluation set.
Further analysis validates that model ensembling effectively combines the strengths of different models, enhancing both robustness and accuracy. These findings contribute to advancing the field of audio anti-spoofing, particularly in SVDD. Future work can explore further optimization of layer aggregation techniques and broader applications to improve detection systems.








§ ACKNOWLEDGEMENTS

This work is supported
by the National Research Foundation, Prime Minister’s Office, Singapore, and the Ministry of Digital Development and Information, under its Online Trust and Safety (OTS) Research Programme (MCI-OTS-001). Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Prime Minister’s Office,  Singapore, or the Ministry of Digital Development and Information.














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