Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection

Duc-Tuan Truong, Ruijie Tao, Tuan Nguyen, Hieu-Thi Luong, Kong Aik Lee, Eng Siong Chng·June 25, 2024

Summary

This paper introduces the Temporal-Channel Modeling (TCM) module for enhancing Transformer-based synthetic speech detection systems, particularly in the XLSR-Conformer model. TCM addresses the MHSA's limitation by incorporating head tokens that represent channel information, thereby capturing both temporal and channel dependencies. The module improves the model's performance, achieving a 9.25% EER reduction in ASVspoof 2021 with minimal parameter increase. TCM's effectiveness is demonstrated through experiments, showing its robustness across different architectures and its ability to set new state-of-the-art results. The research also explores related topics in ASR adaptation, spoofing countermeasures, and the use of various techniques to enhance anti-spoofing systems.

Key findings

5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that enhancing the multi-head self-attention (MHSA) model with a Temporal-Channel Modeling (TCM) module can improve the detection of synthetic speech by capturing temporal-channel dependencies . The study focuses on how incorporating both temporal and channel information can enhance the MHSA's ability to detect synthetic speech, leading to a 9.25% improvement in EER compared to the state-of-the-art system .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" proposes several new ideas, methods, and models to enhance synthetic speech detection . Here are the key contributions outlined in the paper:

  1. Temporal-Channel Modeling (TCM) Module: The paper introduces a TCM module designed to improve the performance of the XLSR-Conformer system in detecting synthetic speech . This module aims to capture the temporal-channel dependencies of input sequences, which are crucial for accurately identifying artifacts in synthetic speech. By incorporating both temporal and channel information, the TCM module enhances the interaction between temporal and channel dependencies during multi-head self-attention (MHSA).

  2. Head Tokens Design: The TCM module utilizes a head tokens design, where each head token represents information on the channel dimension . This design enriches the classification token with both temporal and channel information, facilitating the correlation between temporal and channel dependencies within the input sequence. The inclusion of head tokens enhances the performance of the TCM module by improving the interaction between temporal and channel information.

  3. Performance Improvement: Through empirical evaluation, the paper demonstrates that both temporal information from input tokens and channel information from head tokens play a significant role in enhancing the TCM module's performance . The TCM module, with a marginal increase in parameters, boosts the performance of the XLSR-Conformer system on the ASV2021 evaluation set, outperforming the state-of-the-art system by 9.25% in Equal Error Rate (EER) . The TCM module achieves notable improvements in fixed-length input evaluation on both LA and DF tracks, surpassing previous results and achieving a new state-of-the-art EER in the DF track .

  4. Multi-head Attention Analysis: The paper also investigates the impact of different numbers of heads with and without the TCM module on the ASV2021 LA & DF evaluation set . The analysis reveals that the system with 4 heads leads to the best performance, demonstrating the robustness of the improvement brought by the TCM module in most cases. Additionally, the study highlights that an increase in the number of attention heads does not always guarantee improved results .

In summary, the paper introduces the TCM module, leverages the head tokens design, and demonstrates the importance of integrating temporal and channel information for enhancing synthetic speech detection performance, particularly in the context of the XLSR-Conformer system . The "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" paper introduces the Temporal-Channel Modeling (TCM) module, which offers distinct characteristics and advantages compared to previous methods, enhancing synthetic speech detection performance . Here are the key characteristics and advantages highlighted in the paper:

  1. Temporal-Channel Dependency Modeling: The TCM module is designed to address the temporal-channel dependencies present in synthetic speech artifacts, which are often located in specific regions of both frequency channels and temporal segments . Unlike previous methods that may neglect this crucial temporal-channel relationship, the TCM module focuses on capturing these dependencies to improve the detection accuracy of synthetic speech.

  2. Enhanced Multi-head Self-Attention (MHSA): By incorporating the TCM module, the paper enhances the MHSA's capability to capture temporal-channel dependencies within input sequences . This improvement allows for a more comprehensive understanding of the temporal and channel information, leading to better detection of synthetic speech artifacts compared to conventional methods that may not explicitly consider these dependencies.

  3. Performance Improvement: Through empirical evaluation on the ASVspoof 2021 dataset, the TCM module demonstrates significant performance gains compared to the state-of-the-art XLSR-Conformer system and other competitive systems . The TCM module achieves a remarkable 9.25% Equal Error Rate (EER) improvement over the baseline XLSR-Conformer system, showcasing its effectiveness in enhancing synthetic speech detection accuracy.

  4. Efficiency and Lightweight Design: Despite its performance gains, the TCM module is lightweight, adding only 0.03M parameters to the XLSR-Conformer system . This efficiency ensures that the TCM module can enhance detection accuracy without significantly increasing the computational complexity of the system, making it a practical and effective solution for synthetic speech detection tasks.

In summary, the TCM module introduced in the paper offers a novel approach to modeling temporal-channel dependencies, enhances the performance of the MHSA, achieves significant performance improvements over existing methods, and maintains efficiency through its lightweight design, making it a valuable contribution to the field of synthetic speech detection .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of synthetic speech detection. The paper "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" mentions notable researchers such as Duc-Tuan Truong, Ruijie Tao, Tuan Nguyen, Hieu-Thi Luong, Kong Aik Lee, and Eng Siong Chng . The key solution proposed in the paper is the Temporal-Channel Modeling (TCM) module, which enhances the multi-head self-attention's capability to capture temporal-channel dependencies in synthetic speech detection .


How were the experiments in the paper designed?

The experiments in the paper were designed by training two separate Synthetic Speech Detection (SSD) systems with different RawBoost settings to evaluate the LA and DF track, respectively. In the LA track, the SSD system was trained with RawBoost technique combining linear and non-linear convolutive noise and impulsive signal-dependent additive noise strategies. On the other hand, in the DF track, stationary signal-independent additive, randomly colored noise was added during training . These experiments aimed to compare the performance of the proposed Temporal-Channel Modeling (TCM) with the state-of-the-art XLSR-Conformer and other competitive systems on the ASVspoof21 LA and DF evaluation set . The results showed that the TCM module, with only 0.03M additional parameters, outperformed the state-of-the-art system by 9.25% in Equal Error Rate (EER) on the DF track, demonstrating the effectiveness of the proposed method in enhancing the performance of the SSD task .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ASVspoof 2021 logical access (LA) track, which contains clean speech with text-to-speech and voice conversion attacks. The method was evaluated on the ASVspoof 2021 logical access (LA) and deep fake (DF) tasks, which include known and unknown speech data distorted by various codec and compression variations . The information provided does not mention whether the code is open source or not.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified in the context of synthetic speech detection using Temporal-Channel Modeling (TCM) in Multi-head Self-Attention systems . The study demonstrates that the TCM module enhances the capability of Multi-head Self-Attention (MHSA) by capturing temporal-channel dependencies, leading to significant improvements in performance . Specifically, the TCM module outperformed the state-of-the-art system by 9.25% in terms of Equal Error Rate (EER) with only a minimal increase of 0.03M parameters . This indicates that incorporating both temporal and channel information is crucial for detecting synthetic speech effectively .

Furthermore, the results of the experiments show that the proposed TCM module achieved notable improvements in both fixed-length and variable-length utterance evaluations, surpassing previous best-reported results in the DF track by 9.25% . The study also compared the performance of the TCM module with existing competitive systems, demonstrating its effectiveness in enhancing the robustness of the XLSR-Conformer system . The TCM module showed stable improvements for both Conformer and Transformer structures, indicating its versatility and effectiveness in the synthetic speech detection task .

Moreover, the ablation study conducted in the paper analyzed the contributions of each component within the TCM module, highlighting the importance of leveraging both temporal and channel information represented by temporal tokens and head tokens for detecting synthetic speech . The study revealed that excluding key components like head tokens or mean temporal tokens led to a decline in performance, emphasizing the significance of incorporating these elements for optimal system performance . Overall, the experiments and results presented in the paper provide comprehensive evidence supporting the scientific hypotheses related to the effectiveness of the TCM module in enhancing synthetic speech detection systems based on Multi-head Self-Attention mechanisms .


What are the contributions of this paper?

The contributions of the paper "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" include:

  • Proposing a Temporal-Channel Modeling (TCM) module to enhance the multi-head self-attention's capability for capturing temporal-channel dependencies in synthetic speech detection .
  • Demonstrating that utilizing both temporal and channel information leads to significant improvement in detecting synthetic speech, as validated by experimental results on the ASVspoof 2021 dataset .
  • Introducing head tokens to facilitate the correlation between temporal and channel dependencies, enriching the classification token with both temporal and channel information, and improving the performance of the XLSR-Conformer system on the ASV2021 evaluation set .
  • Conducting an ablation study to analyze the contributions of each component within the TCM module, highlighting the importance of leveraging both temporal and channel information represented by temporal tokens and head tokens in synthetic speech detection .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific type of work in mind, feel free to provide more details for a more tailored response.

Tables

3

Introduction
Background
Evolution of Transformer models in speech processing
Challenges in synthetic speech detection
Objective
Introduce TCM as a solution for improving ASVspoof performance
Aim to enhance XLSR-Conformer model specifically
Method
Temporal-Channel Modeling Module
Design
Integration of head tokens for channel representation
Capture of temporal and channel dependencies
Implementation
Modifications to Multi-Head Self-Attention (MHSA) mechanism
Minimal parameter increase
Experiments and Evaluation
Data Collection
ASVspoof 2021 dataset for evaluation
Inclusion of diverse architectures for comparison
Performance Metrics
EER (Equal Error Rate) reduction achieved
State-of-the-art results comparison
Robustness Analysis
Experiments across different architectures
TCM's effectiveness in various scenarios
Results and Discussion
TCM's impact on synthetic speech detection accuracy
Breakdown of performance improvements
Implications for ASR adaptation and spoofing countermeasures
Related Work
Overview of anti-spoofing techniques in ASR
Previous approaches to enhance anti-spoofing systems
Conclusion
Summary of TCM's contributions
Future directions for research in the field
Future Work
Potential extensions to other speech processing tasks
Exploration of TCM in other Transformer-based models
Basic info
papers
sound
audio and speech processing
artificial intelligence
Advanced features
Insights
In what ways does TCM's effectiveness manifest through experimental results, and what are some areas explored in the research?
What is the improvement in EER achieved by the TCM module in ASVspoof 2021, and how does it impact the model's performance?
How does TCM address the limitations of Multi-Head Self-Attention (MHSA) in the XLSR-Conformer model?
What does the Temporal-Channel Modeling (TCM) module aim to achieve in enhancing Transformer-based synthetic speech detection systems?

Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection

Duc-Tuan Truong, Ruijie Tao, Tuan Nguyen, Hieu-Thi Luong, Kong Aik Lee, Eng Siong Chng·June 25, 2024

Summary

This paper introduces the Temporal-Channel Modeling (TCM) module for enhancing Transformer-based synthetic speech detection systems, particularly in the XLSR-Conformer model. TCM addresses the MHSA's limitation by incorporating head tokens that represent channel information, thereby capturing both temporal and channel dependencies. The module improves the model's performance, achieving a 9.25% EER reduction in ASVspoof 2021 with minimal parameter increase. TCM's effectiveness is demonstrated through experiments, showing its robustness across different architectures and its ability to set new state-of-the-art results. The research also explores related topics in ASR adaptation, spoofing countermeasures, and the use of various techniques to enhance anti-spoofing systems.
Mind map
State-of-the-art results comparison
EER (Equal Error Rate) reduction achieved
Inclusion of diverse architectures for comparison
ASVspoof 2021 dataset for evaluation
Minimal parameter increase
Modifications to Multi-Head Self-Attention (MHSA) mechanism
Capture of temporal and channel dependencies
Integration of head tokens for channel representation
TCM's effectiveness in various scenarios
Experiments across different architectures
Performance Metrics
Data Collection
Implementation
Design
Aim to enhance XLSR-Conformer model specifically
Introduce TCM as a solution for improving ASVspoof performance
Challenges in synthetic speech detection
Evolution of Transformer models in speech processing
Exploration of TCM in other Transformer-based models
Potential extensions to other speech processing tasks
Future directions for research in the field
Summary of TCM's contributions
Previous approaches to enhance anti-spoofing systems
Overview of anti-spoofing techniques in ASR
Implications for ASR adaptation and spoofing countermeasures
Breakdown of performance improvements
TCM's impact on synthetic speech detection accuracy
Robustness Analysis
Experiments and Evaluation
Temporal-Channel Modeling Module
Objective
Background
Future Work
Conclusion
Related Work
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of Transformer models in speech processing
Challenges in synthetic speech detection
Objective
Introduce TCM as a solution for improving ASVspoof performance
Aim to enhance XLSR-Conformer model specifically
Method
Temporal-Channel Modeling Module
Design
Integration of head tokens for channel representation
Capture of temporal and channel dependencies
Implementation
Modifications to Multi-Head Self-Attention (MHSA) mechanism
Minimal parameter increase
Experiments and Evaluation
Data Collection
ASVspoof 2021 dataset for evaluation
Inclusion of diverse architectures for comparison
Performance Metrics
EER (Equal Error Rate) reduction achieved
State-of-the-art results comparison
Robustness Analysis
Experiments across different architectures
TCM's effectiveness in various scenarios
Results and Discussion
TCM's impact on synthetic speech detection accuracy
Breakdown of performance improvements
Implications for ASR adaptation and spoofing countermeasures
Related Work
Overview of anti-spoofing techniques in ASR
Previous approaches to enhance anti-spoofing systems
Conclusion
Summary of TCM's contributions
Future directions for research in the field
Future Work
Potential extensions to other speech processing tasks
Exploration of TCM in other Transformer-based models
Key findings
5

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with the title of the paper or a brief description of its topic so that I can assist you better.


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the hypothesis that enhancing the multi-head self-attention (MHSA) model with a Temporal-Channel Modeling (TCM) module can improve the detection of synthetic speech by capturing temporal-channel dependencies . The study focuses on how incorporating both temporal and channel information can enhance the MHSA's ability to detect synthetic speech, leading to a 9.25% improvement in EER compared to the state-of-the-art system .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" proposes several new ideas, methods, and models to enhance synthetic speech detection . Here are the key contributions outlined in the paper:

  1. Temporal-Channel Modeling (TCM) Module: The paper introduces a TCM module designed to improve the performance of the XLSR-Conformer system in detecting synthetic speech . This module aims to capture the temporal-channel dependencies of input sequences, which are crucial for accurately identifying artifacts in synthetic speech. By incorporating both temporal and channel information, the TCM module enhances the interaction between temporal and channel dependencies during multi-head self-attention (MHSA).

  2. Head Tokens Design: The TCM module utilizes a head tokens design, where each head token represents information on the channel dimension . This design enriches the classification token with both temporal and channel information, facilitating the correlation between temporal and channel dependencies within the input sequence. The inclusion of head tokens enhances the performance of the TCM module by improving the interaction between temporal and channel information.

  3. Performance Improvement: Through empirical evaluation, the paper demonstrates that both temporal information from input tokens and channel information from head tokens play a significant role in enhancing the TCM module's performance . The TCM module, with a marginal increase in parameters, boosts the performance of the XLSR-Conformer system on the ASV2021 evaluation set, outperforming the state-of-the-art system by 9.25% in Equal Error Rate (EER) . The TCM module achieves notable improvements in fixed-length input evaluation on both LA and DF tracks, surpassing previous results and achieving a new state-of-the-art EER in the DF track .

  4. Multi-head Attention Analysis: The paper also investigates the impact of different numbers of heads with and without the TCM module on the ASV2021 LA & DF evaluation set . The analysis reveals that the system with 4 heads leads to the best performance, demonstrating the robustness of the improvement brought by the TCM module in most cases. Additionally, the study highlights that an increase in the number of attention heads does not always guarantee improved results .

In summary, the paper introduces the TCM module, leverages the head tokens design, and demonstrates the importance of integrating temporal and channel information for enhancing synthetic speech detection performance, particularly in the context of the XLSR-Conformer system . The "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" paper introduces the Temporal-Channel Modeling (TCM) module, which offers distinct characteristics and advantages compared to previous methods, enhancing synthetic speech detection performance . Here are the key characteristics and advantages highlighted in the paper:

  1. Temporal-Channel Dependency Modeling: The TCM module is designed to address the temporal-channel dependencies present in synthetic speech artifacts, which are often located in specific regions of both frequency channels and temporal segments . Unlike previous methods that may neglect this crucial temporal-channel relationship, the TCM module focuses on capturing these dependencies to improve the detection accuracy of synthetic speech.

  2. Enhanced Multi-head Self-Attention (MHSA): By incorporating the TCM module, the paper enhances the MHSA's capability to capture temporal-channel dependencies within input sequences . This improvement allows for a more comprehensive understanding of the temporal and channel information, leading to better detection of synthetic speech artifacts compared to conventional methods that may not explicitly consider these dependencies.

  3. Performance Improvement: Through empirical evaluation on the ASVspoof 2021 dataset, the TCM module demonstrates significant performance gains compared to the state-of-the-art XLSR-Conformer system and other competitive systems . The TCM module achieves a remarkable 9.25% Equal Error Rate (EER) improvement over the baseline XLSR-Conformer system, showcasing its effectiveness in enhancing synthetic speech detection accuracy.

  4. Efficiency and Lightweight Design: Despite its performance gains, the TCM module is lightweight, adding only 0.03M parameters to the XLSR-Conformer system . This efficiency ensures that the TCM module can enhance detection accuracy without significantly increasing the computational complexity of the system, making it a practical and effective solution for synthetic speech detection tasks.

In summary, the TCM module introduced in the paper offers a novel approach to modeling temporal-channel dependencies, enhances the performance of the MHSA, achieves significant performance improvements over existing methods, and maintains efficiency through its lightweight design, making it a valuable contribution to the field of synthetic speech detection .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related researches exist in the field of synthetic speech detection. The paper "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" mentions notable researchers such as Duc-Tuan Truong, Ruijie Tao, Tuan Nguyen, Hieu-Thi Luong, Kong Aik Lee, and Eng Siong Chng . The key solution proposed in the paper is the Temporal-Channel Modeling (TCM) module, which enhances the multi-head self-attention's capability to capture temporal-channel dependencies in synthetic speech detection .


How were the experiments in the paper designed?

The experiments in the paper were designed by training two separate Synthetic Speech Detection (SSD) systems with different RawBoost settings to evaluate the LA and DF track, respectively. In the LA track, the SSD system was trained with RawBoost technique combining linear and non-linear convolutive noise and impulsive signal-dependent additive noise strategies. On the other hand, in the DF track, stationary signal-independent additive, randomly colored noise was added during training . These experiments aimed to compare the performance of the proposed Temporal-Channel Modeling (TCM) with the state-of-the-art XLSR-Conformer and other competitive systems on the ASVspoof21 LA and DF evaluation set . The results showed that the TCM module, with only 0.03M additional parameters, outperformed the state-of-the-art system by 9.25% in Equal Error Rate (EER) on the DF track, demonstrating the effectiveness of the proposed method in enhancing the performance of the SSD task .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the ASVspoof 2021 logical access (LA) track, which contains clean speech with text-to-speech and voice conversion attacks. The method was evaluated on the ASVspoof 2021 logical access (LA) and deep fake (DF) tasks, which include known and unknown speech data distorted by various codec and compression variations . The information provided does not mention whether the code is open source or not.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that needed to be verified in the context of synthetic speech detection using Temporal-Channel Modeling (TCM) in Multi-head Self-Attention systems . The study demonstrates that the TCM module enhances the capability of Multi-head Self-Attention (MHSA) by capturing temporal-channel dependencies, leading to significant improvements in performance . Specifically, the TCM module outperformed the state-of-the-art system by 9.25% in terms of Equal Error Rate (EER) with only a minimal increase of 0.03M parameters . This indicates that incorporating both temporal and channel information is crucial for detecting synthetic speech effectively .

Furthermore, the results of the experiments show that the proposed TCM module achieved notable improvements in both fixed-length and variable-length utterance evaluations, surpassing previous best-reported results in the DF track by 9.25% . The study also compared the performance of the TCM module with existing competitive systems, demonstrating its effectiveness in enhancing the robustness of the XLSR-Conformer system . The TCM module showed stable improvements for both Conformer and Transformer structures, indicating its versatility and effectiveness in the synthetic speech detection task .

Moreover, the ablation study conducted in the paper analyzed the contributions of each component within the TCM module, highlighting the importance of leveraging both temporal and channel information represented by temporal tokens and head tokens for detecting synthetic speech . The study revealed that excluding key components like head tokens or mean temporal tokens led to a decline in performance, emphasizing the significance of incorporating these elements for optimal system performance . Overall, the experiments and results presented in the paper provide comprehensive evidence supporting the scientific hypotheses related to the effectiveness of the TCM module in enhancing synthetic speech detection systems based on Multi-head Self-Attention mechanisms .


What are the contributions of this paper?

The contributions of the paper "Temporal-Channel Modeling in Multi-head Self-Attention for Synthetic Speech Detection" include:

  • Proposing a Temporal-Channel Modeling (TCM) module to enhance the multi-head self-attention's capability for capturing temporal-channel dependencies in synthetic speech detection .
  • Demonstrating that utilizing both temporal and channel information leads to significant improvement in detecting synthetic speech, as validated by experimental results on the ASVspoof 2021 dataset .
  • Introducing head tokens to facilitate the correlation between temporal and channel dependencies, enriching the classification token with both temporal and channel information, and improving the performance of the XLSR-Conformer system on the ASV2021 evaluation set .
  • Conducting an ablation study to analyze the contributions of each component within the TCM module, highlighting the importance of leveraging both temporal and channel information represented by temporal tokens and head tokens in synthetic speech detection .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific type of work in mind, feel free to provide more details for a more tailored response.

Tables
3
Scan the QR code to ask more questions about the paper
© 2025 Powerdrill. All rights reserved.