Automatic Pronunciation Mistake Detector Using Python
Moin Khan, Pushkar Joglekar, Vedant Mohol, Pranav Modhave, Manas Kadam
speech recognition, phoneme comparison, pitch analysis, pronunciation accuracy, audio processing
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
Article Preview & Download

Share This Article

Join our RMS

Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on

Social Media

Google Verified Reviews