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

Authors: Quchen Fu, Zhongwei Teng, Jules White, Maria Powell, Douglas C. Schmidt

Published: 2021-09-06 23:32:10+00:00

AI Summary

This paper proposes FastAudio, a learnable audio front-end designed for spoof speech detection. It replaces traditional fixed filterbanks with a learnable layer that can adapt to anti-spoofing tasks through joint training with downstream back-ends. FastAudio achieves a significant performance improvement on the ASVspoof 2019 dataset compared to fixed and other learnable front-ends.

Abstract

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.

Key findings

FastAudio achieved a relative improvement of 27% in min t-DCF compared to fixed front-ends on ASVspoof 2019, outperforming other learnable front-ends. The study found that shape constraints are crucial for filterbank learning to prevent overfitting, and high-frequency information, often overlooked by handcrafted features, can be important for spoof speech detection.

Approach

The authors propose FastAudio, an STFT-based learnable audio front-end that uses a learnable triangular filterbank layer instead of fixed filterbanks. This front-end is jointly trained with popular back-end architectures like X-vector and ECAPA-TDNN to adapt features specifically for spoof speech detection.

Datasets

ASVspoof 2019 LA dataset, ASVspoof 2021 LA dataset (preliminary results)

Model(s)

FastAudio (learnable front-end with triangular or Gaussian filterbanks), X-vector (back-end), ECAPA-TDNN (back-end)

Author countries

UNKNOWN

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