Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition

Yicong Jiang, Tianzi Wang, Xurong Xie, Juan Liu, Wei Sun, Nan Yan, Hui Chen, Lan Wang, Xunying Liu, Feng Tian·June 14, 2024

Summary

The paper presents Perceiver-Prompt, a novel approach for speaker adaptation in Chinese dysarthric speech recognition. It fine-tunes the Whisper model using Low-Rank Adaptation (LoRA) and introduces a trainable Perceiver to generate speaker-specific prompts from variable-length inputs. This method addresses data scarcity and speaker variations by leveraging large-scale pre-trained models and P-tuning. Experiments on a Chinese dysarthric dataset show significant improvements in recognition accuracy, with a 13.04% reduction in CER compared to the baseline. The study also explores the impact of severity levels and different configurations, demonstrating the method's effectiveness and adaptability across various dysarthria conditions. The paper contributes to the field by combining acoustic modeling, speaker adaptation, and self-supervised learning to enhance speech recognition for individuals with speech disorders.

Paper digest

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

The paper aims to address the challenges in disordered speech recognition, specifically focusing on dysarthric speech recognition. Dysarthric speech poses difficulties due to limited data availability, significant differences between dysarthric and non-dysarthric speakers, and variations in speech caused by the disorder . While dysarthria itself is not a new problem, the paper introduces a method called Perceiver-Prompt for speaker adaptation to improve the recognition of Chinese dysarthric speech, showcasing consistent performance enhancements . The scarcity of relevant datasets and the need to adapt recognition models to accommodate the unique characteristics of dysarthric speech make this an ongoing and important research problem in the field of speech recognition .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to improving disordered speech recognition, specifically dysarthric speech recognition, by introducing the Perceiver-Prompt method for speaker adaptation in the Whisper large-scale model . The hypothesis revolves around addressing challenges such as limited data, substantial differences between dysarthric and non-dysarthric speakers, and significant speaker variations caused by the disorder . The study focuses on utilizing Perceiver-Prompt to generate fixed-length speaker prompts from variable-length inputs to enhance the model's recognition of Chinese dysarthric speech, with the goal of achieving consistent improvements in recognition performance .


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

The paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" introduces innovative methods and models for speaker adaptation in dysarthric speech recognition tasks . One key contribution is the Perceiver-Prompt method, which utilizes P-Tuning on the Whisper large-scale model to enhance recognition of Chinese dysarthric speech . This method involves fine-tuning Whisper using LoRA and integrating a trainable Perceiver to generate fixed-length speaker prompts from variable-length inputs, resulting in consistent improvements in recognition performance .

The paper explores various configurations and adaptations to optimize the Perceiver-Prompt method for dysarthric speech recognition tasks . By adjusting configurations such as the placement of Perceiver, concatenation positions with inputs, the number of historical speech instances used, and the length of Speaker Prompt, the Perceiver-Prompt method demonstrates superior performance across different setups .

In addition to the Perceiver-Prompt method, the paper discusses the use of LoRA for rapid fine-tuning of large-scale language models, enhancing efficiency and adaptability to specific tasks or domains . The P-Tuning approach, which incorporates trainable prompt embeddings optimized by a prompt encoder into inputs, is highlighted for its efficiency in utilizing limited speaker data, scalability to large-scale models, and flexibility in capturing different information with various configurations .

Furthermore, the paper leverages pre-trained models like Whisper, Hubert, and Wav2Vec 2.0 to compensate for the scarcity of dysarthric speech data . By fine-tuning these pre-trained models, researchers aim to improve recognition performance on dysarthric and elderly speech, addressing challenges encountered in recognizing disordered speech . Methods such as domain-adapted self-supervised learning pre-trained models and speaker adaptation techniques are explored to enhance the robustness of pre-trained models on specific tasks .

Overall, the paper introduces a comprehensive approach that combines innovative methods like Perceiver-Prompt, LoRA fine-tuning, and P-Tuning to advance speaker adaptation in dysarthric speech recognition, aiming to improve recognition performance and address the challenges posed by disordered speech . The Perceiver-Prompt method introduced in the paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" offers several key characteristics and advantages compared to previous methods .

  1. High Flexibility: Perceiver-Prompt demonstrates promising results across various configurations due to its high flexibility. By adjusting configurations such as the placement of Perceiver, concatenation positions with inputs, the number of historical speech instances used, and the length of Speaker Prompt, the method adapts to different tasks and showcases superiority in dysarthric speech recognition tasks .

  2. Improved Performance: Experimental results show that the Perceiver-Prompt method achieves a reduction of 13.04% relative (0.9% absolute) in Character Error Rate (CER) compared to the baseline model, Whisper-medium. It outperforms other methods like i-vector adapted Whisper, Conformer, and TDNN systems without pre-training, particularly demonstrating the best performance for speech samples with higher levels of articulatory disorders .

  3. Efficient Utilization of Limited Data: The P-Tuning approach incorporated in Perceiver-Prompt efficiently utilizes limited speaker data, making it suitable for scenarios with restricted speaker data. This method scales well to large-scale models with billions of parameters and offers flexibility to capture different information with various configurations, enhancing adaptability and performance .

  4. Scalability and Adaptability: Perceiver-Prompt leverages trainable prompt embeddings optimized by a prompt encoder to improve performance without the need for manual prompt design. This approach enhances scalability to large-scale models and ensures adaptability to specific tasks or domains, addressing challenges in disordered speech recognition .

  5. Superiority Over Previous Methods: The Perceiver-Prompt method outperforms other approaches by consistently improving recognition performance in Chinese dysarthric speech tasks. Its ability to generate fixed-length speaker prompts from variable-length inputs, combined with LoRA fine-tuning, contributes to its effectiveness and superiority over conventional methods .

In summary, the Perceiver-Prompt method stands out for its flexibility, performance improvements, efficient data utilization, scalability, adaptability, and overall superiority in dysarthric speech recognition tasks compared to previous methods discussed in the paper.


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 disordered speech recognition, particularly focusing on dysarthric speech. Noteworthy researchers in this field include R. D. Kent , N. M. Joy, S. Umesh , M. Geng, S. Liu, J. Yu, X. Xie, S. Hu, Z. Ye, Z. Jin, H. Meng , P. Swietojanski, J. Li, S. Renals , D. Nguyen, M. Diez, T. Polzehl, L. Burget, J. ˇCernock`y , and many others.

The key to the solution mentioned in the paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" is the Perceiver-Prompt method. This method involves incorporating trainable prompt embeddings optimized by a prompt encoder into inputs for improved performance, eliminating the need for manual prompt design. It is suitable for speaker adaptation in scenarios with limited speaker data, offering advantages such as efficient data utilization per speaker, scalability to large-scale models, and flexibility to capture different information with various configurations .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of the Perceiver-Prompt method in dysarthric speech recognition tasks by conducting experiments with various configurations and settings . The experiments focused on assessing the effectiveness of Perceiver-Prompt in adapting to different tasks by adjusting configurations, such as the placement of Perceiver, concatenation positions with inputs, the number of historical speech instances used, the length of Speaker Prompt, and other configurations . The outcomes of the experiments showcased the superiority of Perceiver-Prompt in dysarthric speech recognition tasks, demonstrating promising results across various configurations due to its high flexibility . The experiments aimed to improve model recognition of Chinese dysarthric speech by utilizing P-Tuning on the Whisper large-scale model and integrating a trainable Perceiver to generate fixed-length speaker prompts from variable-length inputs .


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

The dataset used for quantitative evaluation in the study is a large dataset of 680,000 hours of multilingual and multitask supervised data . The code for the Whisper system, specifically the Whisper-PP method, is open source and available on GitHub under the Coqui TTS repository .


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 need to be verified in the context of dysarthric speech recognition. The research addresses the challenges of limited data availability, significant dissimilarities between dysarthric and non-dysarthric speakers, and variations in speech due to the disorder . The study introduces the Perceiver-Prompt method, which utilizes P-Tuning on the Whisper large-scale model to enhance speaker adaptation for dysarthric speech recognition . Experimental outcomes demonstrate consistent improvements in recognition performance with Perceiver-Prompt, showing a relative reduction of up to 13.04% in Character Error Rate (CER) over the fine-tuned Whisper model .

The paper explores various configurations and training methods, showcasing the flexibility and effectiveness of Perceiver-Prompt in adapting to dysarthric speech recognition tasks . Different experiments on the placement of Perceiver, concatenation positions with inputs, historical speech instances used, and the length of Speaker Prompt highlight the superiority of Perceiver-Prompt in achieving improved recognition performance . The results indicate that targeted adjustments in specific scenarios can lead to enhanced model performance, with the method consistently outperforming the baseline across different configurations .

Furthermore, the study leverages joint training with additional information, such as FDA scores and Speaker identity, to aid Perceiver-Prompt in better adapting to dysarthric speech recognition tasks . The incorporation of auxiliary supervised learning methods demonstrates favorable outcomes, particularly in improving performance on sentence recognition tasks . These findings collectively validate the effectiveness of the proposed Perceiver-Prompt method in addressing the challenges associated with dysarthric speech recognition and support the scientific hypotheses put forth in the research .


What are the contributions of this paper?

The paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" makes several key contributions:

  • Introduces the Perceiver-Prompt method for speaker adaptation, utilizing P-Tuning on the Whisper large-scale model to improve Chinese dysarthric speech recognition .
  • Demonstrates consistent improvements in recognition performance with Perceiver-Prompt, achieving a relative reduction of up to 13.04% in Character Error Rate (CER) over the fine-tuned Whisper model .
  • Explores various configurations of Perceiver-Prompt, showcasing its adaptability by adjusting settings such as the placement of Perceiver, concatenation positions with inputs, historical speech instances used, and the length of Speaker Prompt, leading to superior results in dysarthric speech recognition tasks .
  • Addresses challenges in dysarthric speech recognition, including limited data availability, substantial dissimilarities between dysarthric and non-dysarthric speakers, and significant speaker variations due to the disorder, offering solutions through innovative adaptation techniques .

What work can be continued in depth?

To further advance research in the field of disordered speech recognition, several areas can be explored in depth based on the provided context:

  1. Speaker Adaptation Techniques: Research can delve deeper into exploring innovative speaker adaptation methods to enhance the recognition of dysarthric speech. Techniques such as fine-tuning specific layers of models, utilizing Learning Hidden Unit Contributions (LHUC), and incorporating i-vectors for modeling speaker variability can be further investigated .

  2. Pre-trained Models for Dysarthric Speech Recognition: There is a scope to explore the effectiveness of pre-trained models like Whisper, Hubert, and Wav2Vec 2.0 in compensating for the scarcity of dysarthric speech data. Fine-tuning these pre-trained models and investigating their adaptability to dysarthric speech recognition tasks can be a valuable area of research .

  3. Domain-Adapted Self-Supervised Learning: Further research can focus on incorporating domain-adapted self-supervised learning pre-trained models into speech recognition systems to address challenges encountered in recognizing dysarthric and elderly speech. Exploring methods like x-vector and fMLLR for speaker adaptation on large-scale pre-trained models, such as Whisper, can be an area for deeper exploration .

  4. Efficient Model Performance: Studies can aim to ensure that pre-trained models exhibit robust performance on specific tasks by exploring techniques like LoRA for rapid fine-tuning of large-scale language models and P-Tuning for incorporating trainable prompt embeddings optimized by a prompt encoder. These methods can enhance model efficiency, resource utilization, and performance in adapting to particular tasks or domains .

By focusing on these areas, researchers can contribute to the advancement of disordered speech recognition technology, ultimately improving the quality of life for individuals with speech disorders like dysarthria.

Tables

2

Introduction
Background
Overview of dysarthric speech recognition challenges
Importance of speaker adaptation in this context
Objective
To develop a novel method for adapting pre-trained models to dysarthric speech
Improve recognition accuracy for individuals with speech disorders
Method
Data Collection
Chinese dysarthric speech dataset description
Data scarcity and its impact on model adaptation
Data Preprocessing
Preprocessing techniques for dysarthric speech data
Feature extraction and normalization
Low-Rank Adaptation (LoRA)
Implementation of LoRA for model fine-tuning
Handling limited labeled data
Perceiver Module
Introduction to the Perceiver architecture
Generating speaker-specific prompts using variable-length inputs
P-tuning methodology
Experiments and Configurations
Severity-Level Analysis
Impact of different dysarthria severity levels on recognition
Performance across varying severity conditions
Configuration Variations
Exploration of different Perceiver-Prompt configurations
Evaluation of model robustness and adaptability
Evaluation Metrics
Character Error Rate (CER) as the primary metric
Comparison with baseline models and prior work
Results and Discussion
Quantitative results: CER reduction and accuracy improvements
Comparative analysis with state-of-the-art methods
Limitations and potential improvements
Conclusion
Summary of key findings and contributions
Implications for future research in dysarthric speech recognition
Applications for enhancing speech technology accessibility
Future Work
Suggestions for further improvements and extensions
Potential directions for combining with other self-supervised learning techniques
Basic info
papers
sound
audio and speech processing
artificial intelligence
Advanced features
Insights
What is the improvement in recognition accuracy achieved by Perceiver-Prompt compared to the baseline on the Chinese dysarthric dataset?
What does the study explore regarding the impact of severity levels and different configurations in the Perceiver-Prompt approach?
How does Perceiver-Prompt address data scarcity and speaker variations?
What method does the paper propose for speaker adaptation in Chinese dysarthric speech recognition?

Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition

Yicong Jiang, Tianzi Wang, Xurong Xie, Juan Liu, Wei Sun, Nan Yan, Hui Chen, Lan Wang, Xunying Liu, Feng Tian·June 14, 2024

Summary

The paper presents Perceiver-Prompt, a novel approach for speaker adaptation in Chinese dysarthric speech recognition. It fine-tunes the Whisper model using Low-Rank Adaptation (LoRA) and introduces a trainable Perceiver to generate speaker-specific prompts from variable-length inputs. This method addresses data scarcity and speaker variations by leveraging large-scale pre-trained models and P-tuning. Experiments on a Chinese dysarthric dataset show significant improvements in recognition accuracy, with a 13.04% reduction in CER compared to the baseline. The study also explores the impact of severity levels and different configurations, demonstrating the method's effectiveness and adaptability across various dysarthria conditions. The paper contributes to the field by combining acoustic modeling, speaker adaptation, and self-supervised learning to enhance speech recognition for individuals with speech disorders.
Mind map
Comparison with baseline models and prior work
Character Error Rate (CER) as the primary metric
Evaluation of model robustness and adaptability
Exploration of different Perceiver-Prompt configurations
Performance across varying severity conditions
Impact of different dysarthria severity levels on recognition
P-tuning methodology
Generating speaker-specific prompts using variable-length inputs
Introduction to the Perceiver architecture
Handling limited labeled data
Implementation of LoRA for model fine-tuning
Evaluation Metrics
Configuration Variations
Severity-Level Analysis
Perceiver Module
Low-Rank Adaptation (LoRA)
Data scarcity and its impact on model adaptation
Chinese dysarthric speech dataset description
Improve recognition accuracy for individuals with speech disorders
To develop a novel method for adapting pre-trained models to dysarthric speech
Importance of speaker adaptation in this context
Overview of dysarthric speech recognition challenges
Potential directions for combining with other self-supervised learning techniques
Suggestions for further improvements and extensions
Applications for enhancing speech technology accessibility
Implications for future research in dysarthric speech recognition
Summary of key findings and contributions
Limitations and potential improvements
Comparative analysis with state-of-the-art methods
Quantitative results: CER reduction and accuracy improvements
Experiments and Configurations
Data Preprocessing
Data Collection
Objective
Background
Future Work
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Overview of dysarthric speech recognition challenges
Importance of speaker adaptation in this context
Objective
To develop a novel method for adapting pre-trained models to dysarthric speech
Improve recognition accuracy for individuals with speech disorders
Method
Data Collection
Chinese dysarthric speech dataset description
Data scarcity and its impact on model adaptation
Data Preprocessing
Preprocessing techniques for dysarthric speech data
Feature extraction and normalization
Low-Rank Adaptation (LoRA)
Implementation of LoRA for model fine-tuning
Handling limited labeled data
Perceiver Module
Introduction to the Perceiver architecture
Generating speaker-specific prompts using variable-length inputs
P-tuning methodology
Experiments and Configurations
Severity-Level Analysis
Impact of different dysarthria severity levels on recognition
Performance across varying severity conditions
Configuration Variations
Exploration of different Perceiver-Prompt configurations
Evaluation of model robustness and adaptability
Evaluation Metrics
Character Error Rate (CER) as the primary metric
Comparison with baseline models and prior work
Results and Discussion
Quantitative results: CER reduction and accuracy improvements
Comparative analysis with state-of-the-art methods
Limitations and potential improvements
Conclusion
Summary of key findings and contributions
Implications for future research in dysarthric speech recognition
Applications for enhancing speech technology accessibility
Future Work
Suggestions for further improvements and extensions
Potential directions for combining with other self-supervised learning techniques

Paper digest

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

The paper aims to address the challenges in disordered speech recognition, specifically focusing on dysarthric speech recognition. Dysarthric speech poses difficulties due to limited data availability, significant differences between dysarthric and non-dysarthric speakers, and variations in speech caused by the disorder . While dysarthria itself is not a new problem, the paper introduces a method called Perceiver-Prompt for speaker adaptation to improve the recognition of Chinese dysarthric speech, showcasing consistent performance enhancements . The scarcity of relevant datasets and the need to adapt recognition models to accommodate the unique characteristics of dysarthric speech make this an ongoing and important research problem in the field of speech recognition .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to improving disordered speech recognition, specifically dysarthric speech recognition, by introducing the Perceiver-Prompt method for speaker adaptation in the Whisper large-scale model . The hypothesis revolves around addressing challenges such as limited data, substantial differences between dysarthric and non-dysarthric speakers, and significant speaker variations caused by the disorder . The study focuses on utilizing Perceiver-Prompt to generate fixed-length speaker prompts from variable-length inputs to enhance the model's recognition of Chinese dysarthric speech, with the goal of achieving consistent improvements in recognition performance .


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

The paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" introduces innovative methods and models for speaker adaptation in dysarthric speech recognition tasks . One key contribution is the Perceiver-Prompt method, which utilizes P-Tuning on the Whisper large-scale model to enhance recognition of Chinese dysarthric speech . This method involves fine-tuning Whisper using LoRA and integrating a trainable Perceiver to generate fixed-length speaker prompts from variable-length inputs, resulting in consistent improvements in recognition performance .

The paper explores various configurations and adaptations to optimize the Perceiver-Prompt method for dysarthric speech recognition tasks . By adjusting configurations such as the placement of Perceiver, concatenation positions with inputs, the number of historical speech instances used, and the length of Speaker Prompt, the Perceiver-Prompt method demonstrates superior performance across different setups .

In addition to the Perceiver-Prompt method, the paper discusses the use of LoRA for rapid fine-tuning of large-scale language models, enhancing efficiency and adaptability to specific tasks or domains . The P-Tuning approach, which incorporates trainable prompt embeddings optimized by a prompt encoder into inputs, is highlighted for its efficiency in utilizing limited speaker data, scalability to large-scale models, and flexibility in capturing different information with various configurations .

Furthermore, the paper leverages pre-trained models like Whisper, Hubert, and Wav2Vec 2.0 to compensate for the scarcity of dysarthric speech data . By fine-tuning these pre-trained models, researchers aim to improve recognition performance on dysarthric and elderly speech, addressing challenges encountered in recognizing disordered speech . Methods such as domain-adapted self-supervised learning pre-trained models and speaker adaptation techniques are explored to enhance the robustness of pre-trained models on specific tasks .

Overall, the paper introduces a comprehensive approach that combines innovative methods like Perceiver-Prompt, LoRA fine-tuning, and P-Tuning to advance speaker adaptation in dysarthric speech recognition, aiming to improve recognition performance and address the challenges posed by disordered speech . The Perceiver-Prompt method introduced in the paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" offers several key characteristics and advantages compared to previous methods .

  1. High Flexibility: Perceiver-Prompt demonstrates promising results across various configurations due to its high flexibility. By adjusting configurations such as the placement of Perceiver, concatenation positions with inputs, the number of historical speech instances used, and the length of Speaker Prompt, the method adapts to different tasks and showcases superiority in dysarthric speech recognition tasks .

  2. Improved Performance: Experimental results show that the Perceiver-Prompt method achieves a reduction of 13.04% relative (0.9% absolute) in Character Error Rate (CER) compared to the baseline model, Whisper-medium. It outperforms other methods like i-vector adapted Whisper, Conformer, and TDNN systems without pre-training, particularly demonstrating the best performance for speech samples with higher levels of articulatory disorders .

  3. Efficient Utilization of Limited Data: The P-Tuning approach incorporated in Perceiver-Prompt efficiently utilizes limited speaker data, making it suitable for scenarios with restricted speaker data. This method scales well to large-scale models with billions of parameters and offers flexibility to capture different information with various configurations, enhancing adaptability and performance .

  4. Scalability and Adaptability: Perceiver-Prompt leverages trainable prompt embeddings optimized by a prompt encoder to improve performance without the need for manual prompt design. This approach enhances scalability to large-scale models and ensures adaptability to specific tasks or domains, addressing challenges in disordered speech recognition .

  5. Superiority Over Previous Methods: The Perceiver-Prompt method outperforms other approaches by consistently improving recognition performance in Chinese dysarthric speech tasks. Its ability to generate fixed-length speaker prompts from variable-length inputs, combined with LoRA fine-tuning, contributes to its effectiveness and superiority over conventional methods .

In summary, the Perceiver-Prompt method stands out for its flexibility, performance improvements, efficient data utilization, scalability, adaptability, and overall superiority in dysarthric speech recognition tasks compared to previous methods discussed in the paper.


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 disordered speech recognition, particularly focusing on dysarthric speech. Noteworthy researchers in this field include R. D. Kent , N. M. Joy, S. Umesh , M. Geng, S. Liu, J. Yu, X. Xie, S. Hu, Z. Ye, Z. Jin, H. Meng , P. Swietojanski, J. Li, S. Renals , D. Nguyen, M. Diez, T. Polzehl, L. Burget, J. ˇCernock`y , and many others.

The key to the solution mentioned in the paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" is the Perceiver-Prompt method. This method involves incorporating trainable prompt embeddings optimized by a prompt encoder into inputs for improved performance, eliminating the need for manual prompt design. It is suitable for speaker adaptation in scenarios with limited speaker data, offering advantages such as efficient data utilization per speaker, scalability to large-scale models, and flexibility to capture different information with various configurations .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the performance of the Perceiver-Prompt method in dysarthric speech recognition tasks by conducting experiments with various configurations and settings . The experiments focused on assessing the effectiveness of Perceiver-Prompt in adapting to different tasks by adjusting configurations, such as the placement of Perceiver, concatenation positions with inputs, the number of historical speech instances used, the length of Speaker Prompt, and other configurations . The outcomes of the experiments showcased the superiority of Perceiver-Prompt in dysarthric speech recognition tasks, demonstrating promising results across various configurations due to its high flexibility . The experiments aimed to improve model recognition of Chinese dysarthric speech by utilizing P-Tuning on the Whisper large-scale model and integrating a trainable Perceiver to generate fixed-length speaker prompts from variable-length inputs .


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

The dataset used for quantitative evaluation in the study is a large dataset of 680,000 hours of multilingual and multitask supervised data . The code for the Whisper system, specifically the Whisper-PP method, is open source and available on GitHub under the Coqui TTS repository .


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 need to be verified in the context of dysarthric speech recognition. The research addresses the challenges of limited data availability, significant dissimilarities between dysarthric and non-dysarthric speakers, and variations in speech due to the disorder . The study introduces the Perceiver-Prompt method, which utilizes P-Tuning on the Whisper large-scale model to enhance speaker adaptation for dysarthric speech recognition . Experimental outcomes demonstrate consistent improvements in recognition performance with Perceiver-Prompt, showing a relative reduction of up to 13.04% in Character Error Rate (CER) over the fine-tuned Whisper model .

The paper explores various configurations and training methods, showcasing the flexibility and effectiveness of Perceiver-Prompt in adapting to dysarthric speech recognition tasks . Different experiments on the placement of Perceiver, concatenation positions with inputs, historical speech instances used, and the length of Speaker Prompt highlight the superiority of Perceiver-Prompt in achieving improved recognition performance . The results indicate that targeted adjustments in specific scenarios can lead to enhanced model performance, with the method consistently outperforming the baseline across different configurations .

Furthermore, the study leverages joint training with additional information, such as FDA scores and Speaker identity, to aid Perceiver-Prompt in better adapting to dysarthric speech recognition tasks . The incorporation of auxiliary supervised learning methods demonstrates favorable outcomes, particularly in improving performance on sentence recognition tasks . These findings collectively validate the effectiveness of the proposed Perceiver-Prompt method in addressing the challenges associated with dysarthric speech recognition and support the scientific hypotheses put forth in the research .


What are the contributions of this paper?

The paper "Perceiver-Prompt: Flexible Speaker Adaptation in Whisper for Chinese Disordered Speech Recognition" makes several key contributions:

  • Introduces the Perceiver-Prompt method for speaker adaptation, utilizing P-Tuning on the Whisper large-scale model to improve Chinese dysarthric speech recognition .
  • Demonstrates consistent improvements in recognition performance with Perceiver-Prompt, achieving a relative reduction of up to 13.04% in Character Error Rate (CER) over the fine-tuned Whisper model .
  • Explores various configurations of Perceiver-Prompt, showcasing its adaptability by adjusting settings such as the placement of Perceiver, concatenation positions with inputs, historical speech instances used, and the length of Speaker Prompt, leading to superior results in dysarthric speech recognition tasks .
  • Addresses challenges in dysarthric speech recognition, including limited data availability, substantial dissimilarities between dysarthric and non-dysarthric speakers, and significant speaker variations due to the disorder, offering solutions through innovative adaptation techniques .

What work can be continued in depth?

To further advance research in the field of disordered speech recognition, several areas can be explored in depth based on the provided context:

  1. Speaker Adaptation Techniques: Research can delve deeper into exploring innovative speaker adaptation methods to enhance the recognition of dysarthric speech. Techniques such as fine-tuning specific layers of models, utilizing Learning Hidden Unit Contributions (LHUC), and incorporating i-vectors for modeling speaker variability can be further investigated .

  2. Pre-trained Models for Dysarthric Speech Recognition: There is a scope to explore the effectiveness of pre-trained models like Whisper, Hubert, and Wav2Vec 2.0 in compensating for the scarcity of dysarthric speech data. Fine-tuning these pre-trained models and investigating their adaptability to dysarthric speech recognition tasks can be a valuable area of research .

  3. Domain-Adapted Self-Supervised Learning: Further research can focus on incorporating domain-adapted self-supervised learning pre-trained models into speech recognition systems to address challenges encountered in recognizing dysarthric and elderly speech. Exploring methods like x-vector and fMLLR for speaker adaptation on large-scale pre-trained models, such as Whisper, can be an area for deeper exploration .

  4. Efficient Model Performance: Studies can aim to ensure that pre-trained models exhibit robust performance on specific tasks by exploring techniques like LoRA for rapid fine-tuning of large-scale language models and P-Tuning for incorporating trainable prompt embeddings optimized by a prompt encoder. These methods can enhance model efficiency, resource utilization, and performance in adapting to particular tasks or domains .

By focusing on these areas, researchers can contribute to the advancement of disordered speech recognition technology, ultimately improving the quality of life for individuals with speech disorders like dysarthria.

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