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Objectives: To design and evaluate neural frequency compression method to improve speech intelligibility for Marathi language hearing aid users. Methods/Statistical Analysis: In Recurrent Neural Network Frequency Compression algorithm (RNN-FC), classification and processing are two important stages. After segmentationof input speech into discrete frames. Features are extracted in terms of signal to noise ratio, Pitch, formant frequency and gain frequency spectral coefficient. Extracted features will classify into two segments for processing and improvement in SNR level. Based on classification sample data is divided in two categories; wanted and unwanted samples for processing. Findings: Extracted feature vectors, Training date rate are key performance parameter of RNN-FC method. Testing of RNN-FC was performed on Marathi spoken HA user. In regional Marathi language 14-15 consonants are located over frequency band 7-13.5 kHz. Proposed algorithm shows improvement of classifier with Min 94Max 96% sensitivity, Specificity and Accuracy. Results reports improved recognition rate of Marathi vowel, Consonants and short words. Unwanted vowel, consonants process reduced from 5.67% to 3.56%. The inability to access the high-frequency speech contents in terms of speech and consonant recognition ability enhanced for Marathi HA users. Application/Improvements: Frequency Compression method is extensively adopted by researchers in which high frequency speech is compressed by certain compression factor which causes distortion at lower speech frequencies. Distortion occurred during processing will results in loss of information in Lower band of speech. This challenge is overcomes by using frequency compression approach with neural network classifier (RNN-FC).

Design, improvement and investigation of Neural Frequency Compression method in Hearing Aid for Marathi Speech