# Exploring the Effectiveness of the Phase Features on Double Compressed AMR Speech Detection
## 2 Related Work
In [31], stacked autoencoder (SAE) and dropout networks were utilized for DC AMR-NB speech detection. Here, the long speech signal was first divided into short, non-overlapping segments, which were then fed into the classifier network. A majority voting strategy was employed for determining whether input speech is SC or DC AMR-NB signal. Similarly, [PERSON]. [32] employed a SAE network for feature extraction, followed by modeling extracted features using a Gaussian mixture model (GMM) classifier for DC AMR-NB speech detection. In [33; 34], statistical features extracted from linear predictive coding (LPC) coefficients and line spectral pairs (LSP) of the speech signals were utilized with the SVM classifier for DC AMR-NB speech detection. Given that AMR codec employs LPC analysis for speech signal encoding, the efficacy of LPC coefficient-based features for DC AMR detection, as reported in [30; 31; 33; 34], is unsurprising. Moreover, Ref. [35] demonstrated that reasonable DC AMR-NB speech detection performance can be achieved using traditional spectral features without prior knowledge of encoding algorithms, where simple but effective long-term average spectra (LTAS) features were employed with traditional fully connected DNNs. In [36], the spectrogram of the speech signal was divided into temporal segments, each consisting of 10 speech frames, and LTAS of each segment was computed. These LTAS vectors were then inputted into a DNN classifier for DC AMR-NB speech detection. The introduction of deep convolutional neural networks (CNNs) in [10], utilizing speech spectrogram representation as input features, marked a significant advancement in DC AMR-NB detection. It was demonstrated that both CNNs and spectrogram representations achieved state-of-the-art performance. Subsequently, Ref. [37] proposed the use of angular margin softmax loss functions over vanilla softmax in CNN-based detection systems. It was reported that angular margin losses exhibit superior performance compared to traditional softmax loss. Inspired by the superior performance of spectrogram features on DC AMR-NB speech signal detection, as reported in [10; 37], which are extracted without prior knowledge about the AMR encoder, this paper aims to investigate the potential of phase information in DC AMR speech detection. While previous studies [10; 35; 36] have demonstrated the considerable impact of compressing a speech signal twice on its high-frequency components in the magnitude spectrum, the effects of compression on the phase spectrum remain unexplored. The phase spectrum of a speech signal is known to convey crucial cues not present in the magnitude spectrum and has been extensively utilized in various speech-related recognition tasks, including speaker recognition, speech recognition, language recognition, and synthetic speech detection. However, to the best of our knowledge, the phase spectrum has not been previously utilized for DC speech/audio detection. Therefore, this paper proposes the use of phase features for DC AMR-NB and DC AMR-WB speech signal detection.