Deep learning can be used for audio signal classification in a variety of ways. It can be used to detect and classify various types of audio signals such as speech, music, and environmental sounds. Deep learning models are able to learn complex patterns of audio signals and can be trained on large datasets to achieve high accuracy. To employ deep learning for audio signal classification, the audio signal must first be represented in a suitable form. This can be done using signal representation techniques such as using spectrograms, Mel-frequency Cepstral coefficients, linear predictive coding, and wavelet decomposition. Once the audio signal is represented in a suitable form, it can then be fed into a deep learning model. Various deep learning models can be utilized for audio classification. We provide an extensive survey of current deep learning models used for a variety of audio classification tasks. In particular, we focus on works published under five different deep neural network architectures, namely Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Autoencoders, Transformers and Hybrid Models (hybrid deep learning models and hybrid deep learning models with traditional classifiers). CNNs can be used to classify audio signals into different categories such as speech, music, and environmental sounds. They can also be used for speech recognition, speaker identification, and emotion recognition. RNNs are widely used for audio classification and audio segmentation. RNN models can capture temporal patterns of audio signals and be used to classify audio segments into different categories. Another approach is to use autoencoders for learning the features of audio signals and then classifying the signals into different categories. Transformers are also well-suited for audio classification. In particular, temporal and frequency features can be extracted to identify the characteristics of the audio signals. Finally, hybrid models for audio classification either combine various deep learning architectures (i.e. CNN-RNN) or combine deep learning models with traditional machine learning techniques (i.e. CNN-Support Vector Machine). These hybrid models take advantage of the strengths of different architectures while avoiding their weaknesses. Existing literature under different categories of deep learning are summarized and compared in detail.

The rise of synthetic speech technologies has triggered growing concerns about the increasing difficulty in distinguishing between real and fake voices. In this context, we propose novel hybrid transformer-based models together with different audio feature analysis techniques and achieved the state-of-the-art results. To the best of our knowledge, none of the existing methods have considered combining various hybrid transformer models together with different audio features for fake speech classification, which forms the main novelty of our work. In our work, transformer models are compared with hybrid transformer architectures including Convolutional Neural Network (CNN)-Transformer (i.e., ResNet34- Transformer and VGG16-Transformer models), Bi-directional Long Short-Term Memory (Bi-LSTM)- Transformer, and Transformer with Support Vector Machine (SVM) using different audio feature extraction techniques. In our approach, we utilize three audio attribute extraction techniques: Mel spectrogram (Mel), Mel Frequency Cepstral Coefficient (MFCC), and Short-Time Fourier Transform (STFT) as input representations. The results of our evaluation with instances of real and fake speech using the ASVspoof LA dataset with hybrid transformer models across various audio features indicate that the STFT feature performs best with the ResNet34-Transformer model, achieving a state-of-the-art performance with a development set equal error rate (EER) of 0.0% and evaluation set EER of 3.22%, surpassing all other methods. In terms of accuracy, the STFT feature also performed best with the VGG16-Transformer model, achieving a development set accuracy of 99.55% and an evaluation set accuracy of 94.04%. These results indicate that the proposed study achieved better performance compared to the baseline and state-of-the-art methods.