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Pronunciation Evaluation System

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₹4,999.00

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Detail Description

1. Abstract

The Pronunciation Evaluation System is an AI-powered application designed to evaluate a user's pronunciation and provide feedback to improve speaking skills. Using Automatic Speech Recognition (ASR), speech processing, and Natural Language Processing (NLP), the system analyzes recorded speech and compares it with the expected pronunciation or reference text. It identifies pronunciation errors, evaluates speech clarity, and provides feedback to help users improve their spoken English and language-learning abilities.

2. Objectives

  1. Evaluate users' pronunciation accuracy.
  2. Compare spoken words with reference text or audio.
  3. Identify pronunciation errors and mispronounced words.
  4. Provide feedback to improve speaking skills.
  5. Help learners develop clearer speech and better communication.
  6. Support independent pronunciation practice.

3. Existing System

Traditional pronunciation evaluation depends on teachers, language trainers, or manual listening. Learners may have limited opportunities to receive immediate feedback and may find it difficult to identify their own pronunciation mistakes.

Disadvantages

  1. Requires manual evaluation by a trainer.
  2. Feedback may not be immediately available.
  3. Repeated practice can be time-consuming to assess.
  4. Learners may struggle to identify specific pronunciation errors.
  5. Consistent evaluation can be difficult across large groups of learners.

4. Proposed System

The proposed system uses speech recognition and audio analysis techniques to evaluate spoken words or sentences. Users read a displayed sentence or word aloud, and the system records their speech, compares the recognized output with the reference text, and estimates pronunciation performance. It can provide an overall score, highlight potentially mispronounced words, and suggest areas for improvement. Reliable phoneme-level evaluation requires a model specifically designed for pronunciation assessment; speech-to-text comparison alone cannot fully measure pronunciation quality.

5. Implementation Procedure

  1. Develop a user interface for pronunciation practice.
  2. Display a reference word, sentence, or passage.
  3. Record the user's speech through a microphone.
  4. Preprocess the audio to reduce noise and standardize the format.
  5. Use a speech recognition model to identify the spoken words.
  6. Compare the recognized speech with the reference text.
  7. Analyze pronunciation-related features using suitable speech-processing models.
  8. Calculate an estimated score based on the selected evaluation method.
  9. Display feedback, recognized text, and identified areas for improvement.
  10. Test the system with different speakers, accents, and speaking speeds.

6. Software Requirements

  1. Programming Language: Python
  2. Frontend: Streamlit or React.js
  3. Backend: Flask or FastAPI (optional)
  4. Speech Recognition: Whisper or another compatible ASR model
  5. Audio Processing: Librosa and SoundFile
  6. Pronunciation Assessment: A suitable phoneme-level or pronunciation-scoring model
  7. Machine Learning: PyTorch or TensorFlow, depending on the selected model
  8. Development Environment: VS Code or Jupyter Notebook
  9. Operating System: Windows, Linux, or macOS

7. Hardware Requirements

  1. Processor: Intel Core i3 or equivalent and above
  2. RAM: Minimum 4 GB; 8 GB recommended
  3. Storage: At least 5 GB of free space
  4. Microphone: Required for recording speech
  5. GPU: Optional, depending on the selected model
  6. Internet Connection: Required for cloud-based speech services and model downloads

8. Advantages of the Project

  1. Provides quick feedback on spoken language practice.
  2. Helps learners identify potential pronunciation mistakes.
  3. Encourages independent and repeated practice.
  4. Reduces dependence on continuous manual evaluation.
  5. Supports personalized language-learning activities.
  6. Can improve speech clarity and confidence over time.
  7. Can be extended to support multiple languages.


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