# FastAudio: A Learnable Audio Front-End for Spoof Speech Detection

arXiv: https://arxiv.org/abs/2109.02774 · HTML: https://mlsanity.com/paper/2109.02774/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [LEAF: A Learnable Frontend for Audio Classification](https://mlsanity.com/paper/2101.08596/) (2021)
- [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013)
- [Replay and Synthetic Speech Detection with Res2net Architecture](https://mlsanity.com/paper/2010.15006/) (2020)
- [Learning Filterbanks from Raw Speech for Phone Recognition](https://mlsanity.com/paper/1711.01161/) (2017)
- [DNN Filter Bank Cepstral Coefficients for Spoofing Detection](https://mlsanity.com/paper/1702.03791/) (2017)

## Built on by (most cited first, 8 of 8)

- [SAMO: Speaker Attractor Multi-Center One-Class Learning for Voice Anti-Spoofing](https://mlsanity.com/paper/2211.02718/) (2022)
- [Deep Spectro-temporal Artifacts for Detecting Synthesized Speech](https://mlsanity.com/paper/2210.05254/) (2022)
- [Deep Learning Models on CPUs: A Methodology for Efficient Training](https://mlsanity.com/paper/2206.10034/) (2022)
- [ConvNeXt Based Neural Network for Audio Anti-Spoofing](https://mlsanity.com/paper/2209.06434/) (2022)
- [Real-time Speech Interruption Analysis: From Cloud to Client Deployment](https://mlsanity.com/paper/2210.13334/) (2022)
- [Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method](https://mlsanity.com/paper/2407.10828/) (2024)
- [ISAC: An Invertible and Stable Auditory Filter Bank with Customizable Kernels for ML Integration](https://mlsanity.com/paper/2505.07709/) (2025)
- [LearnAFE: Circuit-Algorithm Co-design Framework for Learnable Audio Analog Front-End](https://mlsanity.com/paper/2507.00755/) (2025)

## Abstract (arXiv)

Voice assistants, such as smart speakers, have exploded in popularity. It is currently estimated that the smart speaker adoption rate has exceeded 35% in the US adult population. Manufacturers have integrated speaker identification technology, which attempts to determine the identity of the person speaking, to provide personalized services to different members of the same family. Speaker identification can also play an important role in controlling how the smart speaker is used. For example, it is not critical to correctly identify the user when playing music. However, when reading the user's email out loud, it is critical to correctly verify the speaker that making the request is the authorized user. Speaker verification systems, which authenticate the speaker identity, are therefore needed as a gatekeeper to protect against various spoofing attacks that aim to impersonate the enrolled user. This paper compares popular learnable front-ends which learn the representations of audio by joint training with downstream tasks (End-to-End). We categorize the front-ends by defining two generic architectures and then analyze the filtering stages of both types in terms of learning constraints. We propose replacing fixed filterbanks with a learnable layer that can better adapt to anti-spoofing tasks. The proposed FastAudio front-end is then tested with two popular back-ends to measure the performance on the LA track of the ASVspoof 2019 dataset. The FastAudio front-end achieves a relative improvement of 27% when compared with fixed front-ends, outperforming all other learnable front-ends on this task.