Abstract
Deep learning algorithms have significantly prevailed as a stand-in for i-vector in numerous applications such as speaker recognition, diarization, segmentation and verification. CNN when fed directly with raw voice fragments have resulted in propitious outcomes. Instead of using conventional hand-crafted features, CNNs learn low-level speech patterns using waveforms, which allows the network to better represent crucial narrow band speaker properties like pitch and formants. These narrow band features are less susceptible to surrounding noise. The design of neural network becomes significant to achieve this purpose. This study suggests the use of SincNet, an unique CNN architecture which enables the first convolutional layer to find deep relevant filters. Band-pass filters are implemented via parameterized sinc functions, on which SincNet is built. The standard CNNs normally learns all elements of each filter, where as the sincnet technique learns only high and low cutoff frequencies directly from raw fed data. This provides a highly compact and effective method to produce a unique filter bank that is precisely tuned for the particular application. Our research on the speaker verification task illustrate that the suggested architecture attains higher speed and performs better than the regular CNN.
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Sanshi, P., Kuruvalli, L.R., Chikkamath, S., Nirmala, R.S. (2023). Deep Learning Framework for Speaker Verification Under Multi Sensor, Multi Lingual and Multi Session Conditions. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) ICT for Intelligent Systems. ICTIS 2023. Smart Innovation, Systems and Technologies, vol 361. Springer, Singapore. https://doi.org/10.1007/978-981-99-3982-4_17
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DOI: https://doi.org/10.1007/978-981-99-3982-4_17
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