Language Discrimination from Speech Signal Using Perceptual and Physical Features
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Humans are the most authoritative language identifiers in the province of speech recognition. They can determine within a glimpse of second whether the language of the hearing speech is known to them or not. This eventuality has come true because of the basic discrimination in the sound pattern characteristics based on frequency, time and perceptual domain. This certitude has provoked the motivation to introduce a potent scheme for the proposed plan. The proposed work includes the identification of three well-spoken languages in India that is English, Hindi and Bengali. The scheme has been encountered using some well known perceptual feature such as pitch along with some physical features like zero-crossing rate (ZCR) of the audio signal. To generate the feature set more efficient, the proposed effort has adopted mel-frequency cepstral coefficients (MFCCs) and the statistical textural features by calculating co-occurrence matrix from MFCC.
KeywordsLanguage identification system (LID) Zero-crossing rate (ZCR) Mel-frequency cepstral coefficient (MFCC)
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