# DNN Filter Bank Cepstral Coefficients for Spoofing Detection

## TL;DR

This paper introduces a new deep neural network filter bank cepstral feature (DNN-FBCC) for detecting spoofed speech, improving robustness against unknown attacks in speaker verification systems.

## Contribution

It proposes a novel learned filter bank feature using a neural network trained with restrictions, capturing speech differences more effectively than traditional methods.

## Key findings

- DNN-FBCC outperforms LFCC in detection accuracy.
- The method is especially effective against unknown spoofing attacks.
- Experimental results on ASVspoof 2015 demonstrate improved reliability.

## Abstract

With the development of speech synthesis techniques, automatic speaker verification systems face the serious challenge of spoofing attack. In order to improve the reliability of speaker verification systems, we develop a new filter bank based cepstral feature, deep neural network filter bank cepstral coefficients (DNN-FBCC), to distinguish between natural and spoofed speech. The deep neural network filter bank is automatically generated by training a filter bank neural network (FBNN) using natural and synthetic speech. By adding restrictions on the training rules, the learned weight matrix of FBNN is band-limited and sorted by frequency, similar to the normal filter bank. Unlike the manually designed filter bank, the learned filter bank has different filter shapes in different channels, which can capture the differences between natural and synthetic speech more effectively. The experimental results on the ASVspoof {2015} database show that the Gaussian mixture model maximum-likelihood (GMM-ML) classifier trained by the new feature performs better than the state-of-the-art linear frequency cepstral coefficients (LFCC) based classifier, especially on detecting unknown attacks.