# Meta Learning Based Deception Detection from Speech
## 4 Comparative Hint Approach Meta-Learning (CHAML)
### Embedding Types
#### 4.1.2 Wav2 Vec 2.0  
Wav2 Vec 2.0 [42] is a framework for self-supervised learning of speech representations. The model training process is divided into two phases; firstly, the model is pre-trained in a self-supervised manner on large quantities of unlabeled audio samples to achieve the best speech representation it can, and secondly, it is fine-tuned on a smaller amount of labeled data.  
The architecture of the model is composed of three parts: feature encoder, context network and quantization module. The feature encoder reduces the dimensionality of the input raw waveform by converting it into a sequence of \(T\) latent audio representations vectors of 25 ms each. It consists of a 1-d convolutional neural network with 7 layers and 512 neurons at each layer. These representations are then fed into both the context network and the quantization module. The context network takes the \(T\) latent audio representations and processes them through Transformer blocks for adding information from the entire audio sequence and getting the contextualized representations. The quantization module discretizes the latent audio representation to a finite set of quantized representations via product quantization. The set of possible quantized representations is composed of a concatenation of codewords sampled from codebooks. The pretraining process uses contrastive learning in which it randomly masks parts of the audio from the latent feature encoder space, which requires the model to identify for each masked frame the correct quantized latent representations from a set of distractors. As mentioned, after the pre-training phase, the model is fine-tuned on labeled data.  
In this study, during the fine-tuning phase, the quantization module is not used. Instead, the model is fine-tuned in a supervised task for speech classification by adding an average pooling layer on top of the context encoder output for calculating the averaged vector from the context representation, followed by a fully connected layer using the Tanh activation function and an output layer with two classes. The hyper-parameters used for fine-tuning the Wav2 Vec 2.0 model on our data are represented in Table 3. We note that the total train batch size was set to the maximal allowed by the GPU used for training, and since fine-tuning the Wav2 Vec 2.0 model required extensive training time, we could not consider many different hyper-parameters. This fine-tuning architecture is inspired by [57] that fine-tunes the Wav2 Vec 2.0 model for the speech classification task.  
The pre-trained model used in our study is the Wav2 Vec 2.0 xlsr-53 model ([[https://huggingface.co/jonatasgrosman/wav2](https://huggingface.co/jonatasgrosman/wav2) vec2-large-xlsr-53-english]([https://huggingface.co/jonatasgrosman/wav2](https://huggingface.co/jonatasgrosman/wav2) vec2-large-xlsr-53-english), accessed 4 October2022) that was fine-tuned on English using the Common Voice [58] data set which currently consists of 7335 h of transcribed speech in 60 different languages. The model outputs an embedding vector of length 1024.