Automatic Pronunciation Mistake Detector Using Python

Automatic Pronunciation Mistake Detector Using Python
Author(s):
Moin Khan, Pushkar Joglekar, Vedant Mohol, Pranav Modhave, Manas Kadam
Keywords:
speech recognition, phoneme comparison, pitch analysis, pronunciation accuracy, audio processing
Abstract
This system focuses on developing a speech recognition tool that compares spoken words to a reference text, evaluating both textual and pitch accuracy. Using the SpeechRecognition library, the system listens to and recognizes spoken words, converting the recognized text and the reference text into phonemes with the CMU Pronouncing Dictionary. The system utilizes the SequenceMatcher from difflib to assess textual similarity and librosa for pitch analysis. By comparing the median pitch and standard deviation of the recorded speech with reference values from a dataset, it adjusts the similarity score based on pitch accuracy. This approach allows the system to provide feedback on pronunciation accuracy, highlighting differences in phonemes and evaluating the overall correctness of the spoken input. The system also handles audio processing and temporarily saves recorded audio for analysis.
Article Details
Unique Paper ID: 164741 Publication Volume & Issue: Volume 10, Issue 12 Page(s): 2085 - 2088
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