Abstract
Speaker recognition is an important task in security applications where a person is recognized through speech input. In speaker recognition, a person is recognized from his or her voice. As no two individuals have same voice and also they have different speaking style, rhythm, tone, etc., speaker is recognized by extracting low-level spectral features and high-level behavioural features. This paper presents a robust speaker recognition approach which combines spectral features and prosodic features to improve the performance. The robust recognition system has been tested under different SNR levels. Two subsystems are implemented (i) speaker recognition based on low-level features such as Mel-frequency cepstral coefficient (MFCC) features. (ii) combined system with MFCC and prosodic features. These subsystems are able to achieve competitive results in classifying different speakers. Experimental results are done on interactive emotional dyadic motion capture database (IEMOCAP). The fusion of low-level and prosodic features achieve approximate 15–20% improvement in accuracy.
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Jagdale, S.M., Shinde, A.A., Chitode, J.S. (2020). Robust Speaker Recognition Based on Low-Level- and Prosodic-Level-Features. In: Jain, V., Chaudhary, G., Taplamacioglu, M., Agarwal, M. (eds) Advances in Data Sciences, Security and Applications. Lecture Notes in Electrical Engineering, vol 612. Springer, Singapore. https://doi.org/10.1007/978-981-15-0372-6_20
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DOI: https://doi.org/10.1007/978-981-15-0372-6_20
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