AI-Powered Stutter Assistant: Single click solution to empower Speech Therapists..

Stuttering, a prevalent speech disorder, impacts nearly 1% of adults globally, translating to about 2 million individuals in Pakistan alone. The conventional assessment and therapy planning for stuttering patients are labor-intensive, time-consuming, and subject to variability in results among speech-language pathologists (SLPs). These processes involve analysis of speech samples to identify and quantify disfluencies, calculate speech rates, and subsequently, devise customized therapy plans. This complexity and the need for precision in diagnostics and treatment planning highlight a critical gap in current practice—a need for an innovative solution to streamline and enhance the efficiency of therapeutic procedures.

To address these issues, a proposed deep tech approach uses audio input to generate reports, reducing speech pathologists' efforts by up to approximately 80%. The system is capable of handling multiple disfluency types and is designed for English and Urdu speaking patients.This deep tech approach utilizes state-of-the-art deep learning models to streamline the assessment process. The system takes the audio input, and through advanced machine learning algorithms, generates reports that are consistent and efficient. Our initial experimental study can be viewed on [link], in which we have proposed a more robust approach to deal with this problem. 

From an application point of view, the speech language pathologists will be able to access the results of their patients in just a single click. The system will require the audio input of the patient, and SLP will be able to review and monitor their prognosis as well, saving them efforts worth hours.

By reducing the manual effort required for stuttering assessments, this technology has the potential to transform the field of speech pathology. It can save time, improve accuracy, and make assessments more accessible to patients who may have difficulty traveling to clinics. Overall, this deep tech approach offers a promising solution to the challenges faced by speech pathologists. Its innovative use of advanced technology has the potential to revolutionize the field, making assessments faster, more accurate, and more accessible than ever before.

In this era of technological advancement, where every thing is just a click away, Speech Language Pathologists still assess their patients with conventional methods. The conventional methods is a long and tedious task as explained in the problem section. Our solution will reduce their efforts by approximately 80%, eventually making the process smoother for the patients as well.

Our solution is designed to cater to a broad spectrum of professionals and institutions within the speech therapy and healthcare sectors. The primary beneficiaries of our system include: Speech Language Pathologist, clinics, hospitals, special schools, speech therapy organizations.

Our team's unique strength lies in our deep connection with the communities we aim to serve, particularly those affected by stuttering in Pakistan. As the Team Lead, my background in speech processing and deep learning, combined with firsthand insights into the regional linguistic nuances and challenges, positions us ideally to address the specific needs of these communities. Our team's composition, including experts in NLP, data analysis, and application development, ensures a comprehensive approach to tackling the complexities of speech disorders.

Further enhancing our alignment with the community, our collaboration with local speech-language pathologists (SLPs) as advisors allows us to ground our technical innovations in practical, real-world applications. These SLPs bring daily experiences from the field, ensuring that our solution is not only technologically advanced but also relevant and practical for everyday use.

The involvement of our technical partner and financial advisor ensures that our approach is sustainable and scalable, addressing both immediate needs and long-term challenges. By incorporating feedback directly from SLPs and patients during the development process, our solution is continuously refined to better meet the specific requirements of the community. This feedback loop is central to our development process, ensuring that the community's input, ideas, and agendas actively guide the evolution of our product.

In our initial experiments, we were able to achieve the highest f1-score of 0.81, with a more robust and resource efficient approach. The detailed results can be seen in the provided link {link to detailed experimentation and results}. 

We have created a working demo, which is in its testing phase. Currently, we are working on a multilingual module of the system. With the existing results, we are in our testing phase to take feedback from speech language pathologists, so that we can improve the system based on their feedback.

We are applying to Solve because we believe it offers a unique platform that aligns perfectly with our mission to revolutionize speech pathology through advanced technology. Our AI-powered stutter assistant tool is designed to significantly enhance the efficiency of speech therapists by reducing the manual assessment effort by approximately 80%. This not only fosters business growth for speech language pathologists by allowing them to handle more cases efficiently but also increases accessibility to quality care for stuttering patients.

Financial Support: While our solution holds strong potential for financial sustainability, initial funding is crucial to expand our reach and impact. We aim to leverage Solve's network for access to seed funding that would allow us to scale our technology, particularly in underserved markets. Financial barriers include the need for capital to enhance our technological infrastructure and expand our operational capacity to reach more therapists and patients.

Technical Collaboration: Solve's ecosystem includes leading innovators and experts in technology and healthcare, whose insights would be invaluable in refining our deep learning models and speech processing algorithms. Technical barriers such as optimizing our tool for low-resource settings and ensuring robust performance across diverse linguistic backgrounds are areas where collaboration with Solve partners could be transformative.

Market Access: We are keen to tap into Solve's global network to navigate market entry complexities and to build partnerships that can facilitate pilot studies and broader deployment in new regions. This includes overcoming cultural and regulatory hurdles that are often challenging for digital health solutions.

Legal and Cultural Guidance: Legal expertise within the Solve community could aid us in navigating the regulatory landscapes of new markets, ensuring compliance with international data protection laws and local health regulations. Cultural insights from the community would help tailor our tool to the specific needs and preferences of different regions, ensuring higher adoption and impact.

By partnering with Solve, we hope to gain not just financial backing but also a wealth of expertise and networking opportunities that can accelerate our development and deployment phases, ensuring our solution reaches those who need it most, effectively and ethically.

Our solution, the AI-powered Stutter Assistant, introduces a groundbreaking approach to stuttering assessment by leveraging advanced artificial intelligence, specifically in the areas of speech processing and deep learning. What makes our tool innovative is its ability to transform traditional, labor-intensive methods into a streamlined, efficient process that reduces the time therapists spend on evaluations by approximately 80%. This is not merely an incremental improvement but a fundamental shift in how speech therapists can operate, leading to significant enhancements in productivity and effectiveness.

Innovative Aspects:

1. Integration of Deep Learning: Utilizing state-of-the-art deep learning models to analyze audio inputs allows our system to detect and classify multiple types of disfluencies.

2. Real-Time Analysis and Feedback: Our solution provides immediate results and feedback, a crucial advantage in therapeutic settings where real-time adjustments can vastly improve treatment outcomes. This immediacy helps therapists make quicker, more informed decisions during therapy sessions.

3. Accessibility and Scalability: Designed to be used easily with minimal training, our tool enables speech therapists, regardless of their technological expertise, to adopt it swiftly. This accessibility can dramatically increase the reach of advanced stuttering therapies to regions and populations that previously had limited access to specialized care.

To articulate a clear theory of change for our AI-powered Stutter Assistant, we outline the steps from our specific activities to the immediate outputs and the eventual long-term outcomes, demonstrating how these elements connect logically to address the problem of stuttering assessments:

1. Development and Implementation of AI Technologies:

   • We utilize deep learning and speech processing technologies to develop a tool that analyzes speech patterns and identifies disfluencies.
   • Integration with user-friendly interfaces for English speaking therapists.

2. Collaboration and Feedback Loops with Speech Language Pathologists (SLPs):

   • Continuous input and feedback from practicing SLPs to refine the technology.
   • Pilot testing in clinical settings to gather data and ensure the tool meets practical needs.

1. Enhanced Assessment Tools for SLPs:

   • Therapists gain access to a tool that automates the detection and analysis of stuttering, which is more efficient than manual methods.

2. Increased Efficiency in Speech Therapy Sessions:

   • Reduction in the time required to assess stuttering from audio samples, allowing more time for direct intervention.

1. Improved Therapist Productivity:

   • Therapists can handle more patients due to time savings, increasing the capacity of healthcare providers to serve more individuals.

2. Consistent and Reliable Assessments:

   • Use of AI ensures that the evaluations are consistent across different therapists and patients, reducing variability in diagnosis and treatment planning.

1. Improved Access to Quality Care:

   • As therapists become more efficient, services become more accessible to stuttering patients, particularly in underserved regions.
   • Technology reduces barriers to accessing specialized stuttering therapy.

2. Better Patient Outcomes:

   • More reliable diagnostics and tailored treatments lead to better therapy outcomes for patients.
   • Increased overall satisfaction and quality of life for individuals affected by stuttering.

• Research and Studies: Evidence from third-party research on AI in healthcare shows that automation and precision technology significantly enhance diagnostic accuracy and treatment effectiveness.
• Pilot Testing Data: Initial tests demonstrate an 80% reduction in assessment time.
• Feedback from SLPs: Direct feedback from early adopters highlights the tool's usability and effectiveness, supporting its acceptance and integration into routine clinical practice.

This logical framework — from the development of AI technologies to better patient outcomes — is grounded in evidence-based practices and ongoing feedback from the field. By systematically linking each activity to tangible outputs and outcomes, we demonstrate a clear pathway through which our solution impacts the problem of stuttering, fostering a transformative change in the field of speech pathology.

Our impact goals for the AI-powered Stutter Assistant focus on enhancing the efficiency and effectiveness of speech therapy for stuttering, particularly by improving accessibility to quality care in underserved populations. Here’s how we define these goals and measure our progress:

1. Increase the Efficiency of Speech Assessments by 80%:

   • Automate the stuttering assessment process to significantly reduce the time therapists spend on evaluations, thereby increasing their capacity for patient interaction and treatment.

2. Improve Access to Speech Therapy in Underserved Areas:

   • Expand the reach of quality stuttering therapy services, particularly to regions with limited access to specialized care.

3. Enhance Patient Outcomes:

   • Provide more consistent and accurate assessments, aiming for improved therapeutic results and better management of stuttering.

4. Integration of Urdu Language Support:

   • As a next step, we plan to integrate Urdu language capabilities into our tool, further broadening our impact by catering to a significant linguistic demographic in Pakistan.

To track progress toward these goals, we employ specific indicators:

1. Reduction in Assessment Time:

   • Indicator: Percentage reduction in average time spent per assessment by therapists using our tool compared to traditional methods.
   • Data Collection: Time logs from sessions using traditional methods and our tool.

2. Number of New Users in Underserved Areas:

   • Indicator: Number of therapists and healthcare facilities in underserved regions adopting the tool.
   • Data Collection: Geographical user data and registration info from healthcare facilities.

3. Improvement in Patient Therapy Outcomes:

   • Indicator: Change in speech fluency and reduction in stuttering frequency over time.
   • Data Collection: Pre- and post-treatment assessments using standardized speech fluency scales.

4. Adoption of Urdu Language Support:

   • Indicator: Implementation and user adoption rate of the Urdu language version of the tool.
   • Data Collection: User feedback, adoption rates, and linguistic efficacy studies post-implementation.

Aligned with the United Nations Sustainable Development Goals, especially SDG 3 (Good Health and Well-being), we consider:

• SDG 3.4.1: Reduction in mortality rate attributed to non-communicable diseases through prevention and treatment, interpreted as improving life quality through better speech disorder management.
• SDG 3.8.1: Coverage of essential health services, related to the accessibility of speech therapy services facilitated by our tool.

We analyze collected data to evaluate our impact and refine our approach through:

• Regular reviews of usage statistics and patient outcome data.
• Surveys and interviews with therapists and patients for qualitative feedback.
• Comparison of baseline data with ongoing data to track improvements and adjust strategies.

These metrics help us monitor our progress and provide insights for continuous improvement, ensuring that our work not only aligns with broader health goals but also makes a measurable difference in the lives of those affected by stuttering, including the Urdu-speaking population.

The core technology driving our AI-powered Stutter Assistant tool is rooted in advanced artificial intelligence, particularly deep learning models. For UI, at the moment we have prepared our demo on streamlit.

We have been working on it since 2 years.

Currently, we are an in-house team, however, we recognize that a diverse team enriches perspectives, fosters innovation, and strengthens our ability to address complex challenges effectively.

B2B Business model, and attached image has further details.