Conformer-Based Stuttering Detection Model Enhanced with LSTM and Multi-Task Learning for Improved Accuracy in Stuttering Assessment

المفاهيم الأساسية

This research paper introduces a novel stuttering detection model that leverages the Conformer model, LSTM networks, and multi-task learning to achieve superior accuracy in identifying and classifying different types of stuttering events.

الملخص

• Bibliographic Information: Liu, X., Xu, C., Yang, Y., Wang, L., & Yan, N. (2024). An End-To-End Stuttering Detection Method Based On Conformer And BILSTM. arXiv preprint arXiv:2406.06584.
• Research Objective: This study aims to develop a more accurate and robust stuttering detection model by combining the strengths of the Conformer model, LSTM networks, and multi-task learning.
• Methodology: The researchers propose a novel model architecture that first utilizes a Conformer encoder to extract acoustic features from stuttered speech. Then, a Long Short-Term Memory (LSTM) network is employed to capture long-term contextual dependencies in the speech signal. Finally, a multi-task learning strategy is implemented to improve model generalization and address data limitations in stuttering research. The model is trained and evaluated on the AS-70 dataset, a Mandarin stuttered speech dataset.
• Key Findings: The proposed Conformer-LSTM model with multi-task learning significantly outperforms existing state-of-the-art stuttering detection methods. The model achieves an average F1 score improvement of 39.8% over the baseline model on the AS-70 dataset. The study also finds that different types of stuttering events benefit from different numbers of LSTM layers and that multi-task learning effectively addresses the overfitting problem in stuttering detection.
• Main Conclusions: The combination of the Conformer model, LSTM networks, and multi-task learning results in a highly effective stuttering detection model. This approach offers a promising solution for improving the accuracy and practicality of stuttering assessment tools.
• Significance: This research makes a significant contribution to the field of stuttering detection by introducing a novel model architecture and demonstrating its superior performance. The findings have important implications for the development of more effective stuttering therapy and support tools.
• Limitations and Future Research: Future research could explore the integration of acoustic signals with semantic information to further enhance model performance. Additionally, the model's application and scalability should be investigated across other stuttering datasets in different languages.

الإحصائيات

Stuttering affects approximately 70 million people worldwide, accounting for about 1% of the total population.
The AS-70 dataset, used in this study, comprises recordings from 70 native Mandarin-speaking adults who stutter, with a gender ratio of 1.9:1 (male:female).
The stuttering event proportions per minute in the AS-70 dataset are 15.58% for dialogues and 8.11% for reading speech commands.
The proposed model achieved an average F1 score improvement of 39.8% over the baseline model on the AS-70 dataset.
Using a Conformer model pre-trained on an ASR dataset resulted in a 13.37% higher average F1 score compared to a Conformer without pre-training.
Increasing the Conformer model layers from 3 to 12 led to performance improvements, but further increases to 15 layers resulted in overfitting.
Multi-task learning improved performance for specific stuttering event types (/b and /r) by 49.58% and 13.33% respectively, while decreasing performance for others (/p, [], and /i) by 3%, 4%, and 18.5% respectively.

اقتباسات

"Stuttering is a major speech disorder affecting approximately 70 million people worldwide, accounting for about 1% of the total population [2], [3]."
"However, due to the heterogeneity and overlap of stuttering behaviors, stuttering event detection (SED) remains challenging, especially when data is limited [5], [6], [7]."
"Experimental results demonstrate that our method outperforms current state-of-the-art techniques for stuttering detection."
"On the AS-70 dataset, our proposed model achieves an average F1 score improvement of 39.8% over the baseline across five tasks."

الرؤى الأساسية المستخلصة من

An End-To-End Stuttering Detection Method Based On Conformer And BILSTM

by Xiaokang Liu... في arxiv.org 11-15-2024

https://arxiv.org/pdf/2411.09479.pdf

استفسارات أعمق

How can this research be translated into practical applications, such as real-time stuttering detection and feedback tools for therapy?

This research holds significant potential for practical applications, particularly in developing real-time stuttering detection and feedback tools for therapy. Here's how:

Real-time Stuttering Detection: The use of Conformer and BILSTM networks, as described in the paper, allows for the capture of both short-term acoustic patterns and long-term contextual dependencies in speech. This is crucial for real-time applications as it enables the model to analyze speech segments and identify stuttering events with minimal delay. This real-time capability can be integrated into various platforms:

Mobile Applications: Imagine a mobile app that listens to a user's speech during conversations or practice sessions. By detecting stuttering events in real-time, the app could provide instant visual or haptic feedback, promoting self-awareness and modification techniques.
Teletherapy Platforms: Integrating this technology into teletherapy platforms could allow speech-language pathologists (SLPs) to monitor a client's fluency remotely. This is particularly valuable for individuals with limited access to in-person therapy.

Personalized Feedback Tools: The model's ability to differentiate between various stuttering symptoms (blocks, prolongations, repetitions, etc.) opens doors for personalized feedback.

Targeted Exercises: Therapy could be tailored based on the specific types of disfluencies detected. For instance, if the model consistently identifies blocks, exercises focusing on airflow and phonation could be prioritized.
Progress Tracking: By analyzing the frequency and types of stuttering events over time, the tool could provide valuable data on a user's progress, motivating continued practice and engagement in therapy.

Augmenting SLP's Capabilities: It's important to note that this technology is not meant to replace SLPs. Instead, it serves as a powerful tool to augment their capabilities.

Reduced Workload: Automating the detection and even classification of stuttering events can free up SLPs to focus on other crucial aspects of therapy, such as developing personalized strategies and providing emotional support.
Objective Assessment: The model can offer a more objective assessment of stuttering severity, supplementing the SLP's clinical judgment and leading to more data-driven treatment plans.
However, translating this research into real-world applications also presents challenges:

Computational Resources: Real-time processing, especially on mobile devices, requires optimization to ensure smooth performance without excessive battery drain.
Accuracy and Robustness: While the model shows promising results, ensuring high accuracy and robustness across diverse accents, speech rates, and background noise is essential for real-world deployment.
User Acceptance and Ethical Considerations: Addressing user concerns regarding privacy, data security, and potential biases in the technology is paramount for successful adoption.

Could the reliance on acoustic features alone limit the model's ability to detect stuttering in cases where acoustic cues are subtle or absent?

Yes, the reliance on acoustic features alone could potentially limit the model's ability to detect stuttering in cases where acoustic cues are subtle or absent. This is because stuttering is a complex disorder with manifestations that extend beyond readily observable acoustic disruptions.

Subtle Stuttering: Some individuals exhibit stuttering symptoms that are less pronounced acoustically. For example, they might experience internal blocks or struggle with word-finding difficulties, which may not manifest as clear pauses or repetitions in the acoustic signal.
Covert Stuttering: In some cases, individuals who stutter develop coping mechanisms to mask their disfluencies. They might substitute words, avoid certain speaking situations, or use circumlocutions, all of which reduce the presence of overt acoustic markers of stuttering.
Linguistic and Psychological Factors: Stuttering is also influenced by linguistic factors (e.g., complexity of sentence structure) and psychological factors (e.g., anxiety, stress). These factors can impact the frequency and severity of stuttering, even in the absence of significant acoustic variations.
To address this limitation, future research could explore:

Multimodal Analysis: Integrating other modalities, such as:

Physiological Signals: Electromyography (EMG) to measure muscle activity during speech production, or electroencephalography (EEG) to capture brain activity patterns associated with stuttering.
Linguistic Features: Analyzing text transcripts for atypical pauses, word repetitions, or grammatical errors that might indicate stuttering.
Eye-Tracking: Monitoring gaze patterns during speech, as individuals who stutter might exhibit different eye movements when encountering difficulties.

Contextual Information: Incorporating contextual information, such as the speaker's emotional state, the social setting, and the topic of conversation, can provide valuable cues about potential stuttering events, even when acoustic markers are subtle.
By moving beyond a purely acoustic-driven approach and embracing a more holistic perspective, stuttering detection models can become more sensitive to the diverse ways in which this disorder manifests, leading to more accurate and comprehensive assessments.

What are the ethical considerations surrounding the development and use of AI-powered stuttering detection technology, particularly in terms of privacy and potential bias?

The development and use of AI-powered stuttering detection technology raise important ethical considerations, particularly concerning privacy and potential bias:
Privacy:

Data Security and Confidentiality: Stuttering detection models require access to potentially sensitive speech data. Ensuring robust data encryption, secure storage, and appropriate anonymization procedures is crucial to protect user privacy.
Informed Consent: Users must be fully informed about how their speech data will be collected, used, stored, and potentially shared. Obtaining explicit consent for data usage, especially for research purposes, is essential.
Data Ownership and Control: Users should have clear ownership rights over their speech data and the ability to access, modify, or delete it as they see fit. Transparency regarding data retention policies is vital.
Potential Bias:

Dataset Bias: If the training data used to develop these models is not diverse and representative of various accents, dialects, and speech patterns, the model might exhibit bias, leading to inaccurate or unfair assessments for certain groups.
Algorithmic Bias: The algorithms themselves can perpetuate or even amplify existing biases present in the data. It's crucial to develop and implement bias mitigation techniques during the model development process.
Risk of Stigmatization: The use of AI-powered stuttering detection technology should not contribute to the stigmatization of individuals who stutter. It's important to promote the understanding that stuttering is a neurological difference and not a personal failing.
To address these ethical considerations, developers and researchers should:

Prioritize Data Diversity and Inclusivity: Actively seek out and incorporate diverse speech data from individuals of different backgrounds, ages, genders, and ethnicities to minimize dataset bias.
Implement Bias Detection and Mitigation Strategies: Regularly audit models for potential biases and employ techniques to mitigate unfair or discriminatory outcomes.
Engage with Stakeholders: Involve speech-language pathologists, ethicists, and individuals who stutter in the development and deployment process to ensure the technology is used responsibly and ethically.
Promote Transparency and Explainability: Make the decision-making processes of these models as transparent and explainable as possible to build trust and accountability.
By proactively addressing these ethical considerations, we can harness the power of AI to develop stuttering detection technology that is not only effective but also equitable, respectful of user privacy, and aligned with the values of inclusivity and fairness.