Abstract
A tremendous growth has been observed in terms of active research in the field of speaker recognition. This has been mainly due to the increasing need of zero-touch interfaces in devices and mobile biometric authentication systems. This paper discusses implementation of text-independent speaker verification system using long short-term memory (LSTM)-based neural network for speaker modeling by using various approaches for the front-end feature extraction including Mel Frequency Spectral Coefficients (MFSC), Mel Frequency Cepstral Coefficients (MFCC), Gammatone Filter Spectra (GTF), and Gammatone Filter Cepstral Coefficients (GFCC). Additionally, to determine the best-suited speaker verification system for given noisy conditions of environment, all the combinational systems are tested under induced noisy conditions with white noise at −20 and −40 dB, as well as under clean environmental condition. The results show that the MFSC-based LSTM-RNN combination tends to perform better than all the other combinations regardless of the noise added in the dataset.
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Dua, M., Sethi, P.S., Agrawal, V., Chawla, R. (2021). Speaker Recognition Using Noise Robust Features and LSTM-RNN. In: Panigrahi, C.R., Pati, B., Pattanayak, B.K., Amic, S., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1299. Springer, Singapore. https://doi.org/10.1007/978-981-33-4299-6_2
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DOI: https://doi.org/10.1007/978-981-33-4299-6_2
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