# Attention-Based LSTM Algorithm for Audio Replay Detection in Noisy Environments
## 2 Related Work  
Over the years, different methods have been tested to explore spoofing detection. A replay-detection algorithm, based on peakmap audio features, was proposed in [4], which calculated the similarity of the peakmap features between the recorded test utterance and the original enrollment one. If the similarity was above a certain threshold, the utterance was determined to be a replay attack. The replay-detection performance was further improved by adopting a relative similarity score in [7].  
[PERSON] et al. used Mel frequency cepstral coefficients (MFCCs) to detect replay [8]. They believed that the silent segments of a speech would better detect the channel source than the spoken segments. Therefore, voice activation detection (VAD) was utilized to extract the silent segments of a speech and a universal background model (UBM) was created to model the difference between the speaker's testing utterance and their enrollment utterance. However, the use of acoustic signal processing to detect replay attacks is considered to be challenging due to the unpredictable changes in voice recording environments, recording equipment, etc. [3]. For example, artifacts introduced by the acoustic surroundings, such as reverberation, might be confused with the artifacts introduced by playback in some cases. [PERSON] et al. [9] tried to use machine learning to detect replay attacks, but only obtained poor performance due to overfitting.  
In the ASVspoof 2017 challenge, an official corpus for detecting replay attack and a baseline system, based on the Gaussian mixture model (GMM) with constant Q cepstral coefficient (CQCC) features [10], were provided. The challenge required participants to propose a method that distinguished between genuine speech and a replay recording, where a total of 49 submissions were received from the participants. The average EER of all the submissions was 26.01% [3], where only 20 out of 49 submissions obtained lower EERs than the GMM and CQCC baseline. Among all of the submissions, the best detection performance was reached when using deep convolutional neural networks on spectrograms [11]. This method used an ensemble of three techniques: convolutional neural network (CNN) with recurrent neural network (RNN), light CNN (LCNN), and support vector machine (SVM) i-vector. In addition, [PERSON] et al. [12] proposed variable length Teager energy operator-energy separation algorithm-instantaneous frequency cosine coefficients (VESA-IFCC), and [PERSON] et al. [9] utilized a deep neural network (DNN) model with L-Fbanks cepstral coefficient (LFCC) features to identify the genuine and the spoofing speech. All these algorithms achieved better results when compared to the other submissions on the dataset.  
The work in this paper is based on the official corpus of the ASVspoof 2017 challenge, i.e., ASVspoof2017 dataset Version 2.0. However, the genuine utterances of the dataset were collected in clean environments (i.e., without any background noises), while the spoof utterances were recorded in environments with various background noises. The genuine utterance setting conflicts with real-world scenarios with complex, noisy environments where the ASV system typically works. In fact,genuine utterances may have background noises, while spoof utterances will contain both the genuine utterances' noises and the noises of the recording environment. Therefore, noisy environments will definitely create challenges in distinguishing between genuine and spoof utterances. Therefore, it is worth exploring the effectiveness of a noise-robust algorithm on spoofing detection, which is the motivation of this paper.