Improving Language Learning Outcomes with Real-Time Speech Recognition
A leading digital education provider wanted to enhance its language-learning platform with real-time pronunciation feedback and interactive speech evaluation. Their existing system relied heavily on manual assessments and delayed feedback cycles, slowing learner progress and limiting scalability.
By implementing IBM Watson Speech to Text, integrated and deployed with Nexright’s expertise, the organization built a robust AI-powered pronunciation engine capable of analyzing speech instantly, identifying errors, and generating actionable recommendations for each learner. This significantly improved learning outcomes, user engagement, and the platform’s ability to scale globally.
Business challenge
As user demand grew, the organization struggled with the limitations of its manual and semi-automated speech evaluation process. Teachers could not provide real-time correction to thousands of learners, which resulted in inconsistent user experiences and higher operational burden.
Key Challenges:
- Inability to scale manual pronunciation assessment across thousands of daily active users.
- Delayed feedback cycles, slowing language acquisition and learner confidence.
- Inconsistent scoring accuracy across instructors and sessions.
- Lack of automation, making it difficult to expand into new regions and languages.
- Limited insights into learner performance trends, preventing personalized recommendations
The organization required an AI-driven, real-time speech recognition platform to automate evaluation, improve accuracy, and provide consistent learning experiences.
Solution
Partnering with Nexright, the company implemented IBM Watson Speech to Text as the core engine for its AI-powered pronunciation and fluency evaluation module. Nexright designed and deployed a scalable architecture that seamlessly integrates Watson Speech to Text into the mobile and web learning applications.
Solution Highlights:
- Real-Time Pronunciation Analysis
Learners receive instant feedback on pronunciation accuracy, tone, speed, and fluency. - Automated Scoring Framework
Nexright developed a machine-learning-based scoring system using Watson transcripts to generate consistent and unbiased evaluations. - Multi-Dialect & Multi-Language Support
Watson Speech to Text enabled rapid expansion into new markets with minimal retraining. - Adaptive Feedback Engine
Integrated NLP models identify common error patterns and tailor hints for each learner. - Scalable Cloud Deployment
Deployed using a containerized architecture to handle high peak volumes during online tutoring sessions.
This end-to-end solution enabled the organization to transform its learning experience—moving from delayed, manual processes to instant AI-driven insights.
Solution components
- IBM Watson Speech to Text
- IBM Watson Natural Language Understanding (optional integration)
- IBM Cloud
Real-Time Speech Recognition
Instant transcription and analysis of learner speech for immediate correction.
Contextual Pronunciation Scoring
AI evaluates not just individual words but full sentences, tone, and emphasis.
Scalable Multi-Tenant Architecture
Supports thousands of learners simultaneously without performance issues.
Result
- 40% faster learner progression, due to real-time feedback replacing delayed manual assessments.
- 60% reduction in support workload, as AI handles evaluations previously done by instructors.
- Improved pronunciation accuracy by 35% in the first four weeks of usage.
- Global deployment readiness, enabling fast expansion across regions and dialects.
- Higher learner satisfaction scores, thanks to instant, objective, and personalized correction.
Watson Speech to Text transformed the way we support language learners. Real-time feedback has created a more engaging and effective learning journey. With Nexright’s seamless integration, we now deliver consistent, scalable speech evaluation across all our users.
— Director of Product Innovation, Digital Education Platform