innsikt - Speech and Language Processing - # Dysarthric Wake-Up Word Spotting

Improving Dysarthric Speech Recognition: An End-to-End Approach for the SLT 2024 Low-Resource Dysarthric Wake-Up Word Spotting Challenge

Q: How can the proposed PD-DWS system be further improved to handle more severe cases of dysarthria or expand to other languages?

The proposed Pretrain-based Dual-filter Dysarthria Wake-up Word Spotting (PD-DWS) system can be enhanced to better accommodate severe cases of dysarthria and to support additional languages through several strategies: Data Augmentation and Synthesis: To address the variability in severe dysarthric speech, the system can benefit from more extensive data augmentation techniques. This includes generating synthetic dysarthric speech using advanced Text-to-Speech (TTS) models that can mimic various levels of dysarthria. By training on a broader range of dysarthric speech samples, the model can learn to recognize more nuanced speech patterns. Personalized Models: Implementing personalized models that adapt to individual users' speech characteristics can significantly improve recognition accuracy. This could involve collecting user-specific data and fine-tuning the model to better understand their unique speech patterns, especially in cases of severe dysarthria. Multilingual Support: To expand the system's capabilities to other languages, a multilingual training approach can be adopted. This involves collecting diverse datasets from speakers of different languages and dialects, ensuring that the model is trained on a variety of phonetic and linguistic features. Additionally, leveraging transfer learning techniques can help in adapting the existing model to new languages with limited data. Enhanced Feature Extraction: Exploring advanced feature extraction methods, such as using phonetic and articulatory features, can improve the model's ability to recognize dysarthric speech. This could involve integrating acoustic features that are more robust to the distortions caused by dysarthria. Continuous Learning: Implementing a continuous learning framework where the model can learn from new data over time can help it adapt to evolving speech patterns, particularly in users with progressive conditions.

Q: What are the potential challenges and limitations in deploying such a system in real-world applications, and how can they be addressed?

Deploying the PD-DWS system in real-world applications presents several challenges and limitations: Variability in Speech: Dysarthric speech can vary significantly among individuals, making it difficult for a single model to generalize effectively. To address this, the system should incorporate user-specific training and continuous adaptation mechanisms to learn from individual speech patterns. Environmental Noise: Real-world environments often contain background noise that can interfere with speech recognition. Implementing robust noise-cancellation techniques and training the model on noisy datasets can enhance its performance in such conditions. User Acceptance and Usability: Users may be hesitant to adopt new technology, especially if it requires extensive training or adaptation. Ensuring that the system is user-friendly, with intuitive interfaces and minimal setup requirements, can improve acceptance. Providing clear instructions and support can also facilitate user engagement. Data Privacy and Security: Collecting and processing personal speech data raises privacy concerns. Implementing strong data protection measures, such as anonymization and secure storage, is essential to safeguard user information. Integration with Existing Technologies: The system must be compatible with various devices and platforms to be widely adopted. Developing APIs and ensuring interoperability with existing assistive technologies can enhance its usability.

Q: How can the insights from this work on dysarthric speech recognition be applied to other areas of speech and language processing, such as assistive technologies or inclusive design?

The insights gained from the development of the PD-DWS system can be applied to various domains within speech and language processing: Assistive Technologies: The techniques used in dysarthric speech recognition can be adapted for other speech disorders, enhancing the capabilities of assistive technologies. For instance, similar models can be developed for individuals with aphasia or other speech impairments, improving communication aids and devices. Inclusive Design: The principles of inclusive design can be informed by the challenges faced in recognizing dysarthric speech. By understanding the diverse needs of users with speech impairments, designers can create more accessible interfaces and applications that cater to a wider audience, ensuring that technology is usable by everyone. Speech Recognition in Noisy Environments: The dual-filter strategy employed in the PD-DWS system can be beneficial in other speech recognition applications, particularly in noisy environments. This approach can be integrated into general speech recognition systems to improve accuracy in challenging acoustic conditions. Personalization in Speech Technologies: The emphasis on personalized models for dysarthric speech recognition can inspire similar approaches in other areas of speech technology, such as virtual assistants and customer service bots. Tailoring these systems to individual user preferences and speech patterns can enhance user experience and satisfaction. Cross-Language Applications: The multilingual training strategies developed for the PD-DWS system can be leveraged in broader speech recognition applications, facilitating the development of systems that support multiple languages and dialects, thus promoting global accessibility.

Grunnleggende konsepter

An end-to-end system named Pretrain-based Dual-filter Dysarthria Wake-up word Spotting (PD-DWS) is proposed to address the challenge of low-resource dysarthric wake-up word spotting, achieving state-of-the-art performance.

Sammendrag

This paper presents an end-to-end system called Pretrain-based Dual-filter Dysarthria Wake-up word Spotting (PD-DWS) for the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting Challenge. The key aspects of the system are:

Audio Modeling:

The system introduces an innovative 2branch-d2v2 model, which is based on the pre-trained data2vec2 (d2v2) model and can simultaneously model automatic speech recognition (ASR) and wake-up word spotting (WWS) tasks through a unified multi-task finetuning paradigm.
Dynamic augmentation techniques, including audio volume variation, noise addition, and speed perturbation, are employed to enhance the model's robustness.

Dual-Filter Strategy:

The threshold filter module processes the probabilities assigned to the ten wake-up words from the WWS branch and determines the temporal scores and labels.
The ASR filter module utilizes the ASR outputs from the model's ASR branch as well as a finetuned Paraformer model to refine the WWS results, improving the overall system performance.

TTS Generator:

A VITS-based TTS model is trained using both control and uncontrol data to generate synthetic dysarthric audio, which is then used to finetune the Paraformer module for better adaptation to the dysarthric environment.

The experimental results demonstrate that the proposed PD-DWS system achieves an FAR of 0.00321 and an FRR of 0.005, with a total score of 0.00821 on the test-B eval set, securing first place in the challenge.

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Statistikk

The LRDWWS dataset comprises 18,630 recordings totaling 17 hours, including 10,125 recordings from non-dysarthric speakers (7.6 hours) and 8,505 recordings from dysarthric speakers (9.4 hours).
The dataset features speech from 21 dysarthric speakers (12 female, 9 male) and 25 non-dysarthric speakers (13 female, 12 male).

Sitater

"Speech has emerged as a widely embraced user interface across diverse applications. However, for individuals with dysarthria, the inherent variability in their speech poses significant challenges."
"By recognizing dysarthric speech, the communication and interaction abilities of people with this disorder can be significantly enhanced, thereby improving their overall quality of life."

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Optimizing Dysarthria Wake-Up Word Spotting: An End-to-End Approach for SLT 2024 LRDWWS Challenge

by Shuiyun Liu,... klokken arxiv.org 09-17-2024

https://arxiv.org/pdf/2409.10076.pdf

Optimizing Dysarthria Wake-Up Word Spotting: An End-to-End Approach for SLT 2024 LRDWWS Challenge

Dypere Spørsmål

How can the proposed PD-DWS system be further improved to handle more severe cases of dysarthria or expand to other languages?

The proposed Pretrain-based Dual-filter Dysarthria Wake-up Word Spotting (PD-DWS) system can be enhanced to better accommodate severe cases of dysarthria and to support additional languages through several strategies:

Data Augmentation and Synthesis: To address the variability in severe dysarthric speech, the system can benefit from more extensive data augmentation techniques. This includes generating synthetic dysarthric speech using advanced Text-to-Speech (TTS) models that can mimic various levels of dysarthria. By training on a broader range of dysarthric speech samples, the model can learn to recognize more nuanced speech patterns.

Personalized Models: Implementing personalized models that adapt to individual users' speech characteristics can significantly improve recognition accuracy. This could involve collecting user-specific data and fine-tuning the model to better understand their unique speech patterns, especially in cases of severe dysarthria.

Multilingual Support: To expand the system's capabilities to other languages, a multilingual training approach can be adopted. This involves collecting diverse datasets from speakers of different languages and dialects, ensuring that the model is trained on a variety of phonetic and linguistic features. Additionally, leveraging transfer learning techniques can help in adapting the existing model to new languages with limited data.

Enhanced Feature Extraction: Exploring advanced feature extraction methods, such as using phonetic and articulatory features, can improve the model's ability to recognize dysarthric speech. This could involve integrating acoustic features that are more robust to the distortions caused by dysarthria.

Continuous Learning: Implementing a continuous learning framework where the model can learn from new data over time can help it adapt to evolving speech patterns, particularly in users with progressive conditions.

What are the potential challenges and limitations in deploying such a system in real-world applications, and how can they be addressed?

Deploying the PD-DWS system in real-world applications presents several challenges and limitations:

Variability in Speech: Dysarthric speech can vary significantly among individuals, making it difficult for a single model to generalize effectively. To address this, the system should incorporate user-specific training and continuous adaptation mechanisms to learn from individual speech patterns.

Environmental Noise: Real-world environments often contain background noise that can interfere with speech recognition. Implementing robust noise-cancellation techniques and training the model on noisy datasets can enhance its performance in such conditions.

User Acceptance and Usability: Users may be hesitant to adopt new technology, especially if it requires extensive training or adaptation. Ensuring that the system is user-friendly, with intuitive interfaces and minimal setup requirements, can improve acceptance. Providing clear instructions and support can also facilitate user engagement.

Data Privacy and Security: Collecting and processing personal speech data raises privacy concerns. Implementing strong data protection measures, such as anonymization and secure storage, is essential to safeguard user information.

Integration with Existing Technologies: The system must be compatible with various devices and platforms to be widely adopted. Developing APIs and ensuring interoperability with existing assistive technologies can enhance its usability.

How can the insights from this work on dysarthric speech recognition be applied to other areas of speech and language processing, such as assistive technologies or inclusive design?

The insights gained from the development of the PD-DWS system can be applied to various domains within speech and language processing:

Assistive Technologies: The techniques used in dysarthric speech recognition can be adapted for other speech disorders, enhancing the capabilities of assistive technologies. For instance, similar models can be developed for individuals with aphasia or other speech impairments, improving communication aids and devices.

Inclusive Design: The principles of inclusive design can be informed by the challenges faced in recognizing dysarthric speech. By understanding the diverse needs of users with speech impairments, designers can create more accessible interfaces and applications that cater to a wider audience, ensuring that technology is usable by everyone.

Speech Recognition in Noisy Environments: The dual-filter strategy employed in the PD-DWS system can be beneficial in other speech recognition applications, particularly in noisy environments. This approach can be integrated into general speech recognition systems to improve accuracy in challenging acoustic conditions.

Personalization in Speech Technologies: The emphasis on personalized models for dysarthric speech recognition can inspire similar approaches in other areas of speech technology, such as virtual assistants and customer service bots. Tailoring these systems to individual user preferences and speech patterns can enhance user experience and satisfaction.

Cross-Language Applications: The multilingual training strategies developed for the PD-DWS system can be leveraged in broader speech recognition applications, facilitating the development of systems that support multiple languages and dialects, thus promoting global accessibility.