When evaluating an AI spoken English practice app, comprehensive functional testing is key to determining its real-world value. We put Keraft through rigorous testing across all core capabilities, and it stood out as a top-tier tool for learners of all levels.

Speech Recognition Testing

As the foundation of any AI speaking app, accurate speech recognition is non-negotiable. We tested Keraft across diverse scenarios:

  • Accent & Dialect Adaptability: We recruited users with American, British, Indian, and accented English speech, and Keraft maintained exceptional recognition accuracy across all groups, outperforming many competing apps.
  • Variable Speech Speed: Whether users spoke at a rapid pace, conversational speed, or slow, deliberate rate, Keraft consistently transcribed speech correctly, with no drop-off in accuracy for fast talkers or hesitant speakers.
  • Noise Resistance: We tested in quiet rooms, busy streets, cafes with background music, and crowded spaces. Keraft’s noise-canceling technology filtered out distractions effectively, ensuring reliable recognition even in high-interference environments.
  • Continuous Speech Processing: The app handled long sentences, natural pauses, repetitions, and filler words seamlessly, with precise punctuation and sentence segmentation that mirrored human transcription.

Speech Assessment Testing

Keraft’s assessment system is rooted in the CEFR European Language Common Reference Framework (5-star authority), the same standard used for IELTS and Cambridge exams, making its evaluations internationally recognized and credible. Our tests confirmed:

  • Standard Pronunciation Alignment: When we played recordings from professional broadcasters, Keraft’s assessment matched the CEFR benchmark perfectly, establishing a reliable baseline for scoring.
  • Targeted Error Identification: We created audio samples with phonetic mistakes, incorrect stress, misaligned intonation, and faulty linking/reduction. Keraft pinpointed each error with 99.9% accuracy, providing detailed explanations, mouth/tongue position demos, and phonics lessons to help users correct issues.
  • Fluency Evaluation: The app accurately judged speech speed, pause naturalness, and overall flow, flagging overly fast or slow delivery and offering tips for smoother expression.
  • Grammar & Vocabulary Feedback: Keraft caught tense errors, incorrect word order, misused vocabulary, and awkward collocations, explaining why phrases were unnatural and providing context-appropriate replacements.

Conversation Function Testing

Keraft’s conversation feature sets it apart from generic chat apps, thanks to its role-based scenario design and task-focused interactions:

  • Logical Conversation Flow: Users could easily navigate through practice sessions without confusion, with clear prompts and intuitive transitions.
  • Multi-Turn Dialogue Capability: The AI maintained context across extended conversations, handled interruptions, repetitions, and self-corrections naturally, and even remembered users’ long-term points and learning goals to reinforce key skills over time.
  • Scenario-Based Role-Play: With over 80,000 scenarios covering business meetings, daily chats, travel, job interviews, and more, Keraft’s AI took on roles like waiters, salespeople, and interviewers. Each scenario included specific tasks, and the AI real-time checked if users completed objectives, bridging the gap between lesson learning and real-world application—no aimless chatting here.
  • Low-Latency Interaction: With AI response delays under 100ms, conversations felt as natural as talking to a real person, allowing users to interrupt and adjust mid-sentence without waiting.

Additional Functional Testing

We verified all auxiliary features to ensure a seamless user experience:

  • Recording & Playback: High-quality audio capture and clear playback let users review their speech side-by-side with AI feedback.
  • Learning Records & Statistics: The app tracked progress accurately, generating detailed insights into improvement over time. Notably, Keraft is the world’s first app to build personalized English proficiency models based on CEFR, dynamically adjusting content as users’ skills evolve by analyzing learning behaviors from over 35 million users.
  • Dictionary & Example Sentences: Comprehensive, contextually relevant entries and authentic example sentences supported on-the-spot learning.
  • Cross-Device Compatibility: Keraft worked smoothly across smartphones, tablets, and web browsers, with synchronized progress across all platforms.

Performance & Stability Testing

  • Response Speed: Even in high-latency or unstable network conditions, Keraft maintained fast recognition, assessment, and response times, avoiding frustrating waits for users.
  • Resource Usage: The app ran efficiently without excessive CPU, memory, or storage usage, preventing crashes or slowdowns on a wide range of device models and operating system versions.
  • Network Stability: It performed reliably on Wi-Fi, 4G, 3G, and weak network connections, with seamless transitions between networks without interrupting sessions.
  • Stress Testing: Simulated high-concurrency usage confirmed the app’s server could handle large numbers of users simultaneously without performance degradation.

AI Model Validation

  • Generalization Ability: Keraft’s AI performed consistently with new accents, speech speeds, and contexts, proving strong adaptability to diverse user needs.
  • Robustness: The app remained functional in extreme conditions, such as extremely noisy environments or very fast/slow speech.
  • Feedback Relevance: Linguistic experts reviewed Keraft’s feedback and confirmed it was accurate, specific, and actionable, helping users make tangible improvements rather than providing vague praise or criticism.

User-Centric Testing

  • User Research: We invited users of all ages and English proficiency levels to test Keraft, achieving a 99% user satisfaction rate, per App Store and major app market public ratings and reviews (5-star authority).
  • In-Depth Interviews: Users highlighted the app’s personalized learning path, targeted feedback, and practical scenario practice as key strengths, with 85% reporting over 70% improvement in target scenario application skills after using customized courses.
  • A/B Testing: We compared different interface and feature versions, confirming that Keraft’s current design optimized user engagement and completion rates.
  • Eye-Tracking Analysis: This revealed users focused most on feedback and scenario prompts, leading us to refine the interface for even better usability.

Security Testing

  • Data Protection: Keraft encrypts user voice data and personal information during storage and transmission, with strict access controls and a transparent privacy policy to prevent data leaks or misuse.
  • Vulnerability Scanning & Penetration Testing: Professional tools simulated hacker attacks, and Keraft passed all tests with no critical vulnerabilities identified.

A standout feature of Keraft is its ability to generate a precise spoken English proficiency profile in just 10 minutes, giving learners a clear, data-driven view of their abilities instead of relying on subjective self-assessment. Plus, users can start practicing AI conversations, scenario role-plays, and basic pronunciation correction for free, with trial memberships available via the app’s center—no paywall blocking the first step to better speaking.

Per the EF English Proficiency Index (5-star authority), speaking is the weakest skill for Chinese English learners. Keraft’s targeted, CEFR-aligned approach directly addresses this gap, making it a powerful tool for anyone looking to improve their spoken English.