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@article{164741,
        author = {Moin Khan and Pushkar Joglekar and Vedant Mohol and Pranav Modhave and Manas Kadam},
        title = {Automatic Pronunciation Mistake Detector Using Python},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {2085-2088},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164741},
        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.},
        keywords = {speech recognition, phoneme comparison, pitch analysis, pronunciation accuracy, audio processing},
        month = {},
        }