Who should use the Pronunciation Assessment workflow?
Teams or solo builders working on learning tasks who want a repeatable process instead of one-off tool experiments.
AI Workflow · Learning
Practical execution plan for pronunciation assessment with clear steps, mapped tools, and delivery-focused outcomes.
Deliverable outcome
A polished, actionable assessment delivered to the learner with clear next steps for improvement.
30-90 minutes
Includes setup plus initial result generation
Free to start
You can swap tools by pricing and policy requirements
A polished, actionable assessment delivered to the learner with clear next steps for improvement.
Use each step output as the input for the next stage
Step map
Instead of relying on a single generic AI model, this pipeline connects specialized tools to maximize quality. First, you'll use ELSA Speak to a clear rubric and target phoneme list ready for assessment creation. Then, you pass the output to Azure Speech Studio to a set of prompts and a functional recording setup ready for the learner. Then, you pass the output to Audacity (Noise Reduction & AI Suppression) to clean, normalized audio files for each prompt, ready for automated analysis. Then, you pass the output to Google Cloud Speech-to-Text to a per-prompt score for accuracy, fluency, and intelligibility, plus an overall average. Then, you pass the output to Gemini 2.5 Pro to a personalized, actionable report that the learner can use to target specific pronunciation errors. Then, you pass the output to Duolingo to final scores that are validated by human judgment, increasing trust and accuracy. Finally, Area9 Lyceum is used to a polished, actionable assessment delivered to the learner with clear next steps for improvement.
Define Assessment Criteria and Target Phonemes
A clear rubric and target phoneme list ready for assessment creation.
Prepare Assessment Prompts and Recording Setup
A set of prompts and a functional recording setup ready for the learner.
Collect and Preprocess Learner Audio
Clean, normalized audio files for each prompt, ready for automated analysis.
Run Automated Phoneme Recognition and Scoring
A per-prompt score for accuracy, fluency, and intelligibility, plus an overall average.
Generate Diagnostic Feedback Report
A personalized, actionable report that the learner can use to target specific pronunciation errors.
Conduct Human Review and Refine Scores (Optional)
Final scores that are validated by human judgment, increasing trust and accuracy.
Deliver Final Assessment and Next Steps
A polished, actionable assessment delivered to the learner with clear next steps for improvement.
Identify the specific phonemes, word stress patterns, and intonation contours that will be assessed. Choose a reference accent (e.g., General American, Received Pronunciation) and decide on scoring dimensions: accuracy, fluency, and intelligibility.
Why ELSA Speak: ELSA Speak provides phoneme-level pronunciation correction and intonation/stress pattern analysis, directly supporting the creation of assessment criteria and target phoneme identification.
Design 5-10 short prompts (words, sentences, or minimal pairs) that elicit the target phonemes. Set up a quiet recording environment with a high-quality microphone and ensure the learner can record directly into the assessment tool.
Why Azure Speech Studio: Azure Speech Studio includes audio transcription and real-time translation capabilities, which can assist in preparing and testing assessment prompts and recording setups.
Have the learner record each prompt in sequence. After recording, trim silence from start/end, normalize volume to -3dB, and convert to 16-bit WAV format for analysis.
Why Audacity (Noise Reduction & AI Suppression): Audacity (Noise Reduction & AI Suppression) provides spectral noise subtraction and AI speech isolation, directly supporting audio preprocessing and cleanup of learner recordings.
Feed each audio file into a phoneme recognition engine (e.g., Montreal Forced Aligner, Google Speech-to-Text with phoneme output). Compare recognized phonemes to the expected target and compute accuracy, fluency (speech rate), and intelligibility (confidence score).
Why Google Cloud Speech-to-Text: Google Cloud Speech-to-Text provides real-time streaming transcription and batch audio file processing, essential for automated phoneme recognition and scoring.
Compile scores into a visual report showing strengths (phonemes scored 3) and weaknesses (phonemes scored 1). Include a waveform with highlighted mispronounced segments and a list of recommended practice words.
Why Gemini 2.5 Pro: Gemini 2.5 Pro offers complex multi-step reasoning and code generation, which can assist in generating diagnostic feedback reports and plotting waveforms.
If automated confidence is low (<0.7) or the learner disputes a score, a human evaluator listens to the audio and adjusts the score manually. This step ensures fairness for edge cases like heavy accents or background noise.
Why Duolingo: Duolingo offers pronunciation assessment and conversational roleplay, providing a scoring interface and audio playback for human review and refinement.
Export the report as a PDF or shareable web link. Include a summary of overall pronunciation level (beginner/intermediate/advanced) and a recommended learning path (e.g., 3 focus phonemes to practice next week).
Why Area9 Lyceum: Area9 Lyceum creates adaptive learning paths with AI-driven content sequencing, directly supporting the delivery of final assessments and next steps with personalized learning paths.
§ Before you start
Teams or solo builders working on learning tasks who want a repeatable process instead of one-off tool experiments.
No. Start with the top pick for each step, then replace tools only if they do not fit your pricing, compliance, or output needs.
Open the mapped task page and compare top options side by side. Prioritize output quality, integration fit, and predictable cost before scaling.
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