AccentSense AI
This AI-driven solution allows Alexa to accurately recognize and respond to users’ voice commands without requiring them to adjust their speech patterns.
Problem Statement
How might we improve Amazon Alexa’s ability to accurately understand diverse accents and dialects using AI?
Problem Background
Amazon Alexa is widely recognized for its smart assistant capabilities, allowing users to control smart devices, play music, and retrieve information using voice commands. However, users with regional accents, ethnic dialects, or non-standard English pronunciation often face encounter difficulties in being accurately understood.
This is especially frustrating when requesting specific song titles in African languages or dialect-heavy music genres, as Alexa frequently misinterprets the request and plays incorrect songs. Accent bias in voice assistants impacts user adoption, retention, and satisfaction.
Research Insights
Consumer Interview Questions and Responses
User Pain Points
Using Reddit forums and WhatsApp groups, we identified key frustrations among Amazon Alexa users:
Supporting Data
44.4% of people we surveyed feel frustrated or annoyed when Alexa does not recognize their accent
Feedback
Our preliminary user research to validate this problem with users found that most participants felt excluded from the mainstream voice assistant experience due to a lack of regional accent recognition.
Market Opportunity
Enhancing Alexa’s ability to recognize diverse accents is not just a user experience improvement; it represents a big business opportunity for Amazon.
Market Size & Reach
- United States: Approximately 25 million individuals in the U.S. speak a language other than English at home, representing a large customer segment for Alexa.
- Canada: A significant portion of the population speaks languages other than English or French, creating a need for better multilingual voice recognition.
Customers and Business Impact
Customer Benefits:
- Seamless experience: Users no longer need to modify their speech.
- Enhanced music request accuracy: Non-English and dialect-heavy music titles are correctly recognized.
- Improved daily interactions: Smart home controls, reminders, and entertainment features work without misinterpretation.
- Higher satisfaction and adoption: Increased engagement among users who previously avoided Alexa due to poor recognition.
Business Benefits for Amazon
- Higher retention rates: More users will continue using Alexa instead of switching to competitors.
- Increased usage time per session: Users will rely on Alexa for more tasks without frustration.
- Better customer insights: Improved recognition will allow Amazon to gather better data on diverse speech patterns.
- Revenue growth: Improved usability will lead to increased subscription and device sales.
- Market Expansion: Addressing this gap would help Amazon tap into new and underserved customer bases, both within North America and internationally.
Landing on the Solution
Based on our target users’ pain points, we knew we wanted to work on the following features:
- Accent Adaptive AI – A machine learning model that improves Alexa’s understanding of Nigerian American speech patterns over time.
- Personalized Pronunciation Training – Users can provide verbal corrections when Alexa misinterprets a request.
- Afrobeats Genre Optimization – A dedicated music model that enhances recognition of Afrobeat artist names and song titles.
📌 Prototype Feedback:
💡 Users preferred real-time feedback loops where Alexa learns from mistakes and improves gradually.
User Flows & Mockups
User Flow: Adeola’s Music Request Process
See attached Visual User Flow Diagram.
Low-Fidelity Wireframe
See attached Wireframe of Alexa app’s AccentSense AI improvements.
Future Steps
Phase 1: Nigerian American Accent Optimization (Current Focus)
Phase 2: Expansion to other underrepresented accents (Multilingual, South Asian English
Phase 3: AI-powered voice training integrated into Alexa’s onboarding
Partnerships: Collaborate with streaming services for better song recognition
Short-Term Improvements:
- Expand AI training data to include more African, South Asian, and bilingual English accents.
- Introduce voice training during Alexa onboarding to personalize recognition.
Long-Term Vision:
- Multi-Accent Personalization – Adaptive learning expands to diverse global accents.
Images & Screenshots
Final Thoughts & Next Steps
✔️ Inclusive voice AI creates better experiences for millions of users.
✔️ AccentSense AI enhances engagement, satisfaction, and retention.
✔️ Bringing adaptive AI to Alexa is a strategic move for Amazon’s global expansion.
Learnings
Product Manager Learnings:
Olajumoke Ogbeide
Working on AccentSense AI reinforced the importance of inclusive technology. Through user research and iterative design, I witnessed how small product adjustments can significantly improve the experience for underserved audiences.
Additionally, this project highlighted how AI can optimize efficiency and enhance accessibility.
From a skills perspective, I expanded my toolkit by learning Figma, Lucidspark, and new prioritization frameworks such as R.I.C.E and Effort vs. Reward.
These methodologies helped refine decision-making and ensure a structured approach to feature development
Designer Learnings:
Designer Learnings:
Jo Sturdivant
- Adapting to an Established Team: Joining the team in week 6 of 8 was challenging, as I had to quickly adapt to existing workflows, dynamics, and goals. This mirrors real-world situations where you often integrate into teams mid-project, and flexibility is essential.
- Work-Blocking for Efficiency: With only two weeks to complete the project, I learned the importance of a structured work-blocking system. This approach allowed me to manage my time effectively and meet deadlines under pressure.
- Making Data-Driven Design Decisions: Unlike my past projects, I had to rely on research conducted by others. This was a valuable experience in using pre-existing data to guide design decisions, helping me focus on the core insights without starting from scratch.
Developer Learnings:
Developer Learnings:
Vanady Beard
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As the back-end developer, I learned how important it is to create efficient and reliable systems that support the entire application. This experience also taught me the importance of optimising the database and ensuring the backend is scalable and easy to maintain.
Developer Learnings:
Stephen Asiedu
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As a back-end developer, I've come to understand the importance of being familiar with various database systems and modules. This knowledge enables me to build diverse applications and maintain versatility in my work. I've also learned that the responsibility for making the right choices rests on my shoulders, guided by my best judgement.
Developer Learnings:
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Developer Learnings:
Maurquise Williams
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- Process of Creating an MVP: Developing a Minimum Viable Product (MVP) taught me how to focus on delivering core functionalities balancing between essential features and avoiding scope creep.
- Collaboration in a Real-World Tech Setting: This experience taught me how to collaborate efficiently in a fast-paced tech environment, keeping the team aligned and productive, even while working remotely across time zones.
- Sharpening Critical Thinking and Problem-Solving Skills: This experience honed my ability to think critically and solve problems efficiently. By tackling challenges and finding quick solutions, I sharpened my decision-making and troubleshooting skills in a dynamic, real-world setting.
Developer Learnings:
Jeremiah Williams
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All in all this experience was very awesome I learned that in coding with others being transparent is key
Developers Learnings:
Justin Farley
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I learned how important communication is when working with a team. Communication provides understanding, advice, ideas, and much more. While working with the product team, I’ve found that communication keeps everything flowing smoothly. Working with a team also showed me that every member brings something different to the table and we all have to work together in order to align and meet our end goal.