Speaker Recognition from Raw Waveform with SincNet
Open accessAbstract
Deep learning is progressively gaining popularity as a viable alternative to i-vectors for speaker recognition. Promising results have been recently obtained with Convolutional Neural Networks (CNNs) when fed by raw speech samples directly. Rather than employing standard hand-crafted features, the latter CNNs learn low-level speech representations from waveforms, potentially allowing the network to better capture important narrow-band speaker characteristics such as pitch and formants. Proper design of the neural network is crucial to achieve this goal.This paper proposes a novel CNN architecture, called SincNet, that encourages the first convolutional layer to discover more meaningful filters. SincNet is based on parametrized sinc functions, which implement band-pass filters. In contrast to standard CNNs, that learn all elements of each filter, only low and high cutoff frequencies are directly learned from data with the proposed method. This offers a very compact and efficient way to derive a customized filter bank specifically tuned for the desired application.Our experiments, conducted on both speaker identification and speaker verification tasks, show that the proposed architecture converges faster and performs better than a standard CNN on raw waveforms.
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SincNet employs filters based on parameterised sinc functions, offering a more compact and efficient approach to bandpass filtering; it reduces model complexity while improving performance and convergence speed.
“SincNet [118] employs filters based on parameterised sinc functions, offering a more compact and efficient approach to bandpass filtering. By optimising only the low and high cutoff frequencies of the filters, SincNet significantly reduces model complexity while improving performance and convergence speed.”
From Audio Deepfake Detection: What Has Been Achieved and What Lies Ahead (2025) · cites this paper as [118]
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“Future developments include the application of the proposed architecture to other ERP decoding tasks, and the adoption of interpretable and more lightweight layers, such as the sinc-convolutional layer, to perform band-pass filtering (Ravanelli and Bengio, 2018; Borra et al., 2020b,c).”
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“SincNet [15] is a CNN which can be applied on the raw waveform.”
- Authors
- Ravanelli, M., & Bengio, Y.
- Affiliations
- Université de Montréal (CA)
- Venue
- 2018 IEEE Spoken Language Technology Workshop (SLT)
- Publisher
- IEEE
- Published
- 2018-12
- Pages
- 1021-1028
- OpenAlex
- W2887814324
- Cited by
- 896
- References
- 47
- Citations by year
- 2026: 67 · 2025: 111 · 2024: 116 · 2023: 133 · 2022: 162 · 2021: 144 · 2020: 121 · 2019: 38 · 2018: 3 · 2012: 1
- FWCI
- 31.23
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- public-domain
- Locations
- IEEE Spoken Language Technology Workshop (SLT) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Compute Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Signal Processing › Music and Audio Processing
- Keywords
- speaker recognition, SincNet, convolutional neural networks, bandpass filter, filter bank design, speaker identification, speaker verification
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