Personalized Speech Enhancement Without a Separate Speaker Embedding Model

Tanel Pärnamaa, Ando Saabas·June 14, 2024

Summary

The paper introduces a novel approach to personalized speech enhancement (PSE) that eliminates the need for a separate speaker embedding model. By integrating speaker information within the DeepVQE model, the method simplifies training and deployment, reducing engineering overhead. The technique matches or outperforms existing methods, including the ICASSP 2023 Deep Noise Suppression Challenge winner, in terms of Mean Opinion Score (MOS). It achieves this by using the model's internal embedding for speaker characterization, which facilitates auto-enrollment during real-time audio enhancement without privacy concerns. Experiments compare the proposed method with baselines, showing improved noise reduction, echo suppression, and target speaker preservation. The study highlights the effectiveness of the internal embedding, with the large model outperforming the challenge winner in background quality and the small model offering a better balance between performance and real-time processing. Overall, the research advances PSE techniques for real-time teleconferencing applications.

Paper digest

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

The paper aims to address the issue of personalized speech enhancement (PSE) without the need for a separate speaker embedding model, which is commonly used in existing methods for extracting a speaker's characteristics from enrollment audio. This approach aims to simplify the training and deployment processes by utilizing the internal representation of the PSE model itself as the speaker embedding, eliminating the requirement for a distinct model . This problem is not entirely new, as existing methods typically rely on a separate speaker embedding model for PSE tasks, adding complexity to the overall process .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of a method for personalized speech enhancement without a separate speaker embedding model. The hypothesis focuses on integrating the speaker embedding extraction process into the speech enhancement model itself, eliminating the need for a separate model for speaker information extraction . The study compares this integrated approach with baseline models that utilize different methods for speaker embedding extraction, such as using log-mel filterbank features or a Res2Net model trained for speaker verification . The goal is to assess the overall usefulness of speaker information in speech enhancement and evaluate the improvement of learned features over simple feature extraction .


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

The paper proposes a novel approach in personalized speech enhancement that eliminates the need for a separate speaker embedding model . Instead of relying on a two-stage approach with a separate embedding model for enrollment, the proposed method internally computes the representation of the speaker's voice within the speech enhancement model itself . By utilizing this internal embedding, the model characterizes a speaker's voice profile without the need for an additional embedding model, simplifying the training and deployment of personalized models .

The proposed method offers several advantages, including simplifying the enrollment process by automatically extracting the speaker's embedding from the speech enhancement model being used for audio quality enhancement . This eliminates the need for users to provide an enrollment audio clip separately, streamlining the personalized model usage . Additionally, by extracting the embedding directly from the existing speech enhancement model, computational requirements are minimized as only one model is needed .

To implement this approach, the paper starts with the state-of-the-art speech enhancement model, DeepVQE, and personalizes it using the standard method with a large pre-trained speaker embedding model . Subsequently, the personalized model is trained from scratch using its internal representation for speaker embedding, without altering its architecture or complexity . The results demonstrate that this method matches the performance of the traditional two-stage approach, achieving state-of-the-art results on the Deep Noise Suppression Challenge noise suppression test data . The proposed method in the paper introduces a significant advancement by eliminating the need for a separate speaker embedding model in personalized speech enhancement . This innovative approach internally computes the speaker's voice representation within the speech enhancement model itself, simplifying the training and deployment of personalized models . By utilizing this internal embedding, the model characterizes a speaker's voice profile without the requirement of an additional embedding model, streamlining the personalized model usage .

Compared to previous methods that utilized a two-stage approach with a separate embedding model for enrollment, the proposed method offers several advantages . Firstly, it simplifies the enrollment process by automatically extracting the speaker's embedding from the speech enhancement model being used for audio quality enhancement, eliminating the need for users to provide a separate enrollment audio clip . This change reduces the initial friction of using personalized models and enhances user experience .

Moreover, the proposed method minimizes computational requirements as only one model is needed for both speech enhancement and speaker embedding extraction . This contrasts with the traditional multi-stage and multi-model approach, which involves training, deploying, and maintaining separate models, leading to significant engineering overhead, especially for edge devices . By integrating the speaker embedding extraction within the speech enhancement model, the proposed method simplifies the training and deployment of personalized models .

Additionally, the paper demonstrates that the proposed approach achieves state-of-the-art results on the Deep Noise Suppression Challenge noise suppression test data, matching the performance of the traditional two-stage approach . This highlights the effectiveness and competitiveness of the proposed method in personalized speech enhancement without the reliance on a separate speaker embedding model .


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 research studies exist in the field of personalized speech enhancement without a separate speaker embedding model. Noteworthy researchers in this area include H. Chen, Y. Luo, R. Gu, W. Li, C. Weng , Q. Wang, H. Muckenhirn, K. Wilson, P. Sridhar, Z. Wu, J. R. Hershey, R. A. Saurous, R. J. Weiss, Y. Jia, I. L. Moreno , and A. Sivaraman, M. Kim .

The key to the solution mentioned in the paper involves a novel and simple approach to personalized speech enhancement that eliminates the need for a separate speaker embedding model. The internal representation of the speech enhancement model is utilized as the speaker embedding, leading to improved performance in noise suppression and echo cancellation tasks. This approach achieves state-of-the-art results on the DNS Challenge data and offers a balance between performance and complexity, making it suitable for real-time teleconferencing applications .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of a proposed method for personalized speech enhancement without a separate speaker embedding model . The experimental setup involved comparing the proposed method to multiple baseline models . Three main experimental conditions were established:

  1. Model without a speaker embedding: This condition represented a scenario where the closest speaker to the microphone was extracted from the audio signal. The architecture and size of the enhancement model remained the same in both approaches .
  2. Log-mel filterbank features as speaker embedding: This condition aimed to understand the improvement of learned features over simple feature extraction. It involved computing 80-dim FBANK features, concatenating the temporal mean and standard deviation to get an embedding of size 160 .
  3. Res2Net model trained for speaker verification to extract speaker features: This condition utilized a Res2Net model trained for speaker verification to extract speaker features .

The training data for the experiments was generated following an approach outlined in a previous study, with modifications to include enrollment clips and utilize background speech in addition to background noises . The datasets provided in the ICASSP 2023 AEC and DNS challenges were used, along with the VoxCeleb2 dataset pre-processed with a noise suppressor to remove background noises . The training clips were 40 seconds in length, with 30% of the clips containing background speech .

For evaluation, the AEC Challenge 2023 blind test set was used to assess AEC performance, the DNS Challenge 2023 blind test set for evaluating personalized NS performance, and the AMI dataset for evaluating target speaker . Objective metrics were employed to assess the performance of the models, including echo removal quality, signal degradation quality, echo return loss enhancement, Perceptual Evaluation of Speech Quality (PESQ), and target speaker over-suppression metric .


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

The dataset used for quantitative evaluation in the study on personalized speech enhancement without a separate speaker embedding model is the ICASSP 2022 and 2023 Deep Noise Suppression (DNS) Challenge data . The code for the study is not explicitly mentioned to be open source in the provided context.


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 verification. The study conducted a comprehensive analysis of personalized speech enhancement without a separate speaker embedding model, focusing on various aspects such as target speaker separation, background speech suppression, and over-suppression . The experiments involved evaluating different models, including those with separate and internal embeddings, and comparing their performance using objective metrics like BAK and BAK SUPPR scores .

The results of the experiments demonstrated that the use of speaker embedding significantly improved performance in background speech suppression scenarios, as indicated by the objective metrics . Models utilizing the embedding outperformed those without embeddings and filter-bank based approaches, showcasing the effectiveness of incorporating speaker information in the enhancement process.

Moreover, the study highlighted that the model with an internal embedding yielded comparable or even superior results to the two-stage model in certain metrics, particularly excelling in background speech removal without causing near-end over-suppression . The TSOS metric specifically showed a significant reduction in over-suppressed frames, emphasizing the benefits of the internal embedding approach.

Overall, the experiments and results presented in the paper offer robust empirical evidence supporting the scientific hypotheses under investigation, demonstrating the efficacy of personalized speech enhancement techniques and the importance of considering speaker information in the enhancement process .


What are the contributions of this paper?

The paper makes several contributions in the field of personalized speech enhancement without a separate speaker embedding model:

  • It proposes a method that involves a mixture of local experts without requiring a reference speech utterance during inference, where a separate gating module embeds the audio and selects a specialized expert module based on the speaker, enhancing the speech quality .
  • The paper evaluates the effectiveness of the models by reporting various metrics such as echo removal quality, signal degradation quality, echo return loss enhancement, Perceptual Evaluation of Speech Quality (PESQ), target speaker over-suppression metric, and signal energy reduction in decibels for scenarios with interfering speakers .
  • Additionally, the paper discusses the role of speaker embeddings in target speaker separation, highlighting the effectiveness of log-mel filterbank features in cross-dataset evaluation and the challenges related to over-suppression and background speaker leakage in teleconferencing systems .

What work can be continued in depth?

To delve deeper into the research on personalized speech enhancement without a separate speaker embedding model, further exploration can focus on the following aspects:

  1. Investigating the Impact of Fine-Tuning Speaker Embeddings: Research could continue to explore the effects of fine-tuning speaker embeddings on audio quality, especially in scenarios where transfer from simulated training data to real-world test data is challenging .

  2. Exploring Simplified Personalization Approaches: Further studies can be conducted to refine and evaluate methods that simplify the training and deployment of personalized models by eliminating the need for separate embedding models. This includes investigating the effectiveness of using internal embeddings within the speech enhancement model for speaker characterization .

  3. Enhancing Auto-Enrollment Processes: Future work could focus on streamlining the auto-enrollment process for personalized models by extracting embeddings directly from the speech enhancement model. This approach minimizes computational requirements and simplifies the enrollment process for users, potentially addressing privacy concerns associated with running large speaker embedding models on client devices .

  4. Optimizing Model Architectures: Research efforts can be directed towards optimizing model architectures for personalized speech enhancement. This includes exploring different residual blocks, temporal blocks, and fusion mechanisms to enhance the performance of personalized models in noise suppression and speaker extraction tasks .

By delving into these areas, researchers can advance the field of personalized speech enhancement, improve model performance, and streamline the deployment of personalized audio processing systems.


Introduction
Background
Evolution of speech enhancement techniques
Challenges in traditional PSE methods
Objective
Introducing a novel approach to simplify PSE
Aim to improve performance and real-time processing
Method
DeepVQE Model Integration
Architecture overview
DeepVQE model description
Integration of speaker information
Auto-Enrollment and Privacy Considerations
Real-time auto-enrollment process
Privacy-preserving feature extraction
Performance Metrics
Mean Opinion Score (MOS) evaluation
Comparison with ICASSP 2023 challenge winner
Experiments and Results
Baseline Comparison
Noise reduction performance
Echo suppression effectiveness
Target speaker preservation analysis
Large Model vs. Small Model
Large model: background quality improvement
Small model: performance-realtime trade-off
Real-Time Teleconferencing Application
Suitability for teleconferencing scenarios
Advantages over existing methods
Conclusion
Advancements in PSE technology
Implications for real-world deployment
Future research directions
Limitations and Future Work
Potential drawbacks and improvements
Opportunities for further optimization
Basic info
papers
sound
audio and speech processing
machine learning
artificial intelligence
Advanced features
Insights
What are the key advantages of the technique's auto-enrollment process for real-time audio enhancement?
How does the proposed method compare to the ICASSP 2023 Deep Noise Suppression Challenge winner in terms of MOS?
How does the DeepVQE model integrate speaker information, and what benefits does this integration provide?
What is the primary novelty of the paper's proposed approach to personalized speech enhancement?

Personalized Speech Enhancement Without a Separate Speaker Embedding Model

Tanel Pärnamaa, Ando Saabas·June 14, 2024

Summary

The paper introduces a novel approach to personalized speech enhancement (PSE) that eliminates the need for a separate speaker embedding model. By integrating speaker information within the DeepVQE model, the method simplifies training and deployment, reducing engineering overhead. The technique matches or outperforms existing methods, including the ICASSP 2023 Deep Noise Suppression Challenge winner, in terms of Mean Opinion Score (MOS). It achieves this by using the model's internal embedding for speaker characterization, which facilitates auto-enrollment during real-time audio enhancement without privacy concerns. Experiments compare the proposed method with baselines, showing improved noise reduction, echo suppression, and target speaker preservation. The study highlights the effectiveness of the internal embedding, with the large model outperforming the challenge winner in background quality and the small model offering a better balance between performance and real-time processing. Overall, the research advances PSE techniques for real-time teleconferencing applications.
Mind map
Small model: performance-realtime trade-off
Large model: background quality improvement
Privacy-preserving feature extraction
Real-time auto-enrollment process
Integration of speaker information
DeepVQE model description
Opportunities for further optimization
Potential drawbacks and improvements
Advantages over existing methods
Suitability for teleconferencing scenarios
Large Model vs. Small Model
Comparison with ICASSP 2023 challenge winner
Mean Opinion Score (MOS) evaluation
Auto-Enrollment and Privacy Considerations
Architecture overview
Aim to improve performance and real-time processing
Introducing a novel approach to simplify PSE
Challenges in traditional PSE methods
Evolution of speech enhancement techniques
Limitations and Future Work
Real-Time Teleconferencing Application
Baseline Comparison
Performance Metrics
DeepVQE Model Integration
Objective
Background
Conclusion
Experiments and Results
Method
Introduction
Outline
Introduction
Background
Evolution of speech enhancement techniques
Challenges in traditional PSE methods
Objective
Introducing a novel approach to simplify PSE
Aim to improve performance and real-time processing
Method
DeepVQE Model Integration
Architecture overview
DeepVQE model description
Integration of speaker information
Auto-Enrollment and Privacy Considerations
Real-time auto-enrollment process
Privacy-preserving feature extraction
Performance Metrics
Mean Opinion Score (MOS) evaluation
Comparison with ICASSP 2023 challenge winner
Experiments and Results
Baseline Comparison
Noise reduction performance
Echo suppression effectiveness
Target speaker preservation analysis
Large Model vs. Small Model
Large model: background quality improvement
Small model: performance-realtime trade-off
Real-Time Teleconferencing Application
Suitability for teleconferencing scenarios
Advantages over existing methods
Conclusion
Advancements in PSE technology
Implications for real-world deployment
Future research directions
Limitations and Future Work
Potential drawbacks and improvements
Opportunities for further optimization

Paper digest

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

The paper aims to address the issue of personalized speech enhancement (PSE) without the need for a separate speaker embedding model, which is commonly used in existing methods for extracting a speaker's characteristics from enrollment audio. This approach aims to simplify the training and deployment processes by utilizing the internal representation of the PSE model itself as the speaker embedding, eliminating the requirement for a distinct model . This problem is not entirely new, as existing methods typically rely on a separate speaker embedding model for PSE tasks, adding complexity to the overall process .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the effectiveness of a method for personalized speech enhancement without a separate speaker embedding model. The hypothesis focuses on integrating the speaker embedding extraction process into the speech enhancement model itself, eliminating the need for a separate model for speaker information extraction . The study compares this integrated approach with baseline models that utilize different methods for speaker embedding extraction, such as using log-mel filterbank features or a Res2Net model trained for speaker verification . The goal is to assess the overall usefulness of speaker information in speech enhancement and evaluate the improvement of learned features over simple feature extraction .


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

The paper proposes a novel approach in personalized speech enhancement that eliminates the need for a separate speaker embedding model . Instead of relying on a two-stage approach with a separate embedding model for enrollment, the proposed method internally computes the representation of the speaker's voice within the speech enhancement model itself . By utilizing this internal embedding, the model characterizes a speaker's voice profile without the need for an additional embedding model, simplifying the training and deployment of personalized models .

The proposed method offers several advantages, including simplifying the enrollment process by automatically extracting the speaker's embedding from the speech enhancement model being used for audio quality enhancement . This eliminates the need for users to provide an enrollment audio clip separately, streamlining the personalized model usage . Additionally, by extracting the embedding directly from the existing speech enhancement model, computational requirements are minimized as only one model is needed .

To implement this approach, the paper starts with the state-of-the-art speech enhancement model, DeepVQE, and personalizes it using the standard method with a large pre-trained speaker embedding model . Subsequently, the personalized model is trained from scratch using its internal representation for speaker embedding, without altering its architecture or complexity . The results demonstrate that this method matches the performance of the traditional two-stage approach, achieving state-of-the-art results on the Deep Noise Suppression Challenge noise suppression test data . The proposed method in the paper introduces a significant advancement by eliminating the need for a separate speaker embedding model in personalized speech enhancement . This innovative approach internally computes the speaker's voice representation within the speech enhancement model itself, simplifying the training and deployment of personalized models . By utilizing this internal embedding, the model characterizes a speaker's voice profile without the requirement of an additional embedding model, streamlining the personalized model usage .

Compared to previous methods that utilized a two-stage approach with a separate embedding model for enrollment, the proposed method offers several advantages . Firstly, it simplifies the enrollment process by automatically extracting the speaker's embedding from the speech enhancement model being used for audio quality enhancement, eliminating the need for users to provide a separate enrollment audio clip . This change reduces the initial friction of using personalized models and enhances user experience .

Moreover, the proposed method minimizes computational requirements as only one model is needed for both speech enhancement and speaker embedding extraction . This contrasts with the traditional multi-stage and multi-model approach, which involves training, deploying, and maintaining separate models, leading to significant engineering overhead, especially for edge devices . By integrating the speaker embedding extraction within the speech enhancement model, the proposed method simplifies the training and deployment of personalized models .

Additionally, the paper demonstrates that the proposed approach achieves state-of-the-art results on the Deep Noise Suppression Challenge noise suppression test data, matching the performance of the traditional two-stage approach . This highlights the effectiveness and competitiveness of the proposed method in personalized speech enhancement without the reliance on a separate speaker embedding model .


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 research studies exist in the field of personalized speech enhancement without a separate speaker embedding model. Noteworthy researchers in this area include H. Chen, Y. Luo, R. Gu, W. Li, C. Weng , Q. Wang, H. Muckenhirn, K. Wilson, P. Sridhar, Z. Wu, J. R. Hershey, R. A. Saurous, R. J. Weiss, Y. Jia, I. L. Moreno , and A. Sivaraman, M. Kim .

The key to the solution mentioned in the paper involves a novel and simple approach to personalized speech enhancement that eliminates the need for a separate speaker embedding model. The internal representation of the speech enhancement model is utilized as the speaker embedding, leading to improved performance in noise suppression and echo cancellation tasks. This approach achieves state-of-the-art results on the DNS Challenge data and offers a balance between performance and complexity, making it suitable for real-time teleconferencing applications .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the effectiveness of a proposed method for personalized speech enhancement without a separate speaker embedding model . The experimental setup involved comparing the proposed method to multiple baseline models . Three main experimental conditions were established:

  1. Model without a speaker embedding: This condition represented a scenario where the closest speaker to the microphone was extracted from the audio signal. The architecture and size of the enhancement model remained the same in both approaches .
  2. Log-mel filterbank features as speaker embedding: This condition aimed to understand the improvement of learned features over simple feature extraction. It involved computing 80-dim FBANK features, concatenating the temporal mean and standard deviation to get an embedding of size 160 .
  3. Res2Net model trained for speaker verification to extract speaker features: This condition utilized a Res2Net model trained for speaker verification to extract speaker features .

The training data for the experiments was generated following an approach outlined in a previous study, with modifications to include enrollment clips and utilize background speech in addition to background noises . The datasets provided in the ICASSP 2023 AEC and DNS challenges were used, along with the VoxCeleb2 dataset pre-processed with a noise suppressor to remove background noises . The training clips were 40 seconds in length, with 30% of the clips containing background speech .

For evaluation, the AEC Challenge 2023 blind test set was used to assess AEC performance, the DNS Challenge 2023 blind test set for evaluating personalized NS performance, and the AMI dataset for evaluating target speaker . Objective metrics were employed to assess the performance of the models, including echo removal quality, signal degradation quality, echo return loss enhancement, Perceptual Evaluation of Speech Quality (PESQ), and target speaker over-suppression metric .


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

The dataset used for quantitative evaluation in the study on personalized speech enhancement without a separate speaker embedding model is the ICASSP 2022 and 2023 Deep Noise Suppression (DNS) Challenge data . The code for the study is not explicitly mentioned to be open source in the provided context.


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 verification. The study conducted a comprehensive analysis of personalized speech enhancement without a separate speaker embedding model, focusing on various aspects such as target speaker separation, background speech suppression, and over-suppression . The experiments involved evaluating different models, including those with separate and internal embeddings, and comparing their performance using objective metrics like BAK and BAK SUPPR scores .

The results of the experiments demonstrated that the use of speaker embedding significantly improved performance in background speech suppression scenarios, as indicated by the objective metrics . Models utilizing the embedding outperformed those without embeddings and filter-bank based approaches, showcasing the effectiveness of incorporating speaker information in the enhancement process.

Moreover, the study highlighted that the model with an internal embedding yielded comparable or even superior results to the two-stage model in certain metrics, particularly excelling in background speech removal without causing near-end over-suppression . The TSOS metric specifically showed a significant reduction in over-suppressed frames, emphasizing the benefits of the internal embedding approach.

Overall, the experiments and results presented in the paper offer robust empirical evidence supporting the scientific hypotheses under investigation, demonstrating the efficacy of personalized speech enhancement techniques and the importance of considering speaker information in the enhancement process .


What are the contributions of this paper?

The paper makes several contributions in the field of personalized speech enhancement without a separate speaker embedding model:

  • It proposes a method that involves a mixture of local experts without requiring a reference speech utterance during inference, where a separate gating module embeds the audio and selects a specialized expert module based on the speaker, enhancing the speech quality .
  • The paper evaluates the effectiveness of the models by reporting various metrics such as echo removal quality, signal degradation quality, echo return loss enhancement, Perceptual Evaluation of Speech Quality (PESQ), target speaker over-suppression metric, and signal energy reduction in decibels for scenarios with interfering speakers .
  • Additionally, the paper discusses the role of speaker embeddings in target speaker separation, highlighting the effectiveness of log-mel filterbank features in cross-dataset evaluation and the challenges related to over-suppression and background speaker leakage in teleconferencing systems .

What work can be continued in depth?

To delve deeper into the research on personalized speech enhancement without a separate speaker embedding model, further exploration can focus on the following aspects:

  1. Investigating the Impact of Fine-Tuning Speaker Embeddings: Research could continue to explore the effects of fine-tuning speaker embeddings on audio quality, especially in scenarios where transfer from simulated training data to real-world test data is challenging .

  2. Exploring Simplified Personalization Approaches: Further studies can be conducted to refine and evaluate methods that simplify the training and deployment of personalized models by eliminating the need for separate embedding models. This includes investigating the effectiveness of using internal embeddings within the speech enhancement model for speaker characterization .

  3. Enhancing Auto-Enrollment Processes: Future work could focus on streamlining the auto-enrollment process for personalized models by extracting embeddings directly from the speech enhancement model. This approach minimizes computational requirements and simplifies the enrollment process for users, potentially addressing privacy concerns associated with running large speaker embedding models on client devices .

  4. Optimizing Model Architectures: Research efforts can be directed towards optimizing model architectures for personalized speech enhancement. This includes exploring different residual blocks, temporal blocks, and fusion mechanisms to enhance the performance of personalized models in noise suppression and speaker extraction tasks .

By delving into these areas, researchers can advance the field of personalized speech enhancement, improve model performance, and streamline the deployment of personalized audio processing systems.

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