To further enhance the encoder's representation in the Serialized Output Training (SOT)-based multi-speaker ASR framework, several self-supervised learning (SSL) techniques could be explored:
Contrastive Learning: Techniques such as contrastive learning can be employed to improve the robustness of the encoder's representations. By training the model to distinguish between similar and dissimilar audio segments, it can learn more discriminative features that are beneficial for recognizing overlapping speech.
Masked Language Modeling: Inspired by models like BERT, masked language modeling can be adapted for audio data. By randomly masking portions of the audio input and training the model to predict the missing segments, the encoder can learn to capture contextual information more effectively.
Multi-Modal Learning: Integrating multi-modal learning approaches that utilize both audio and visual data (e.g., lip movements) can enhance the encoder's ability to disambiguate overlapping speech. This could involve training the model on datasets that include both audio and corresponding video, allowing it to leverage visual cues.
Generative Pre-Training: Utilizing generative models, such as Variational Autoencoders (VAEs) or Generative Adversarial Networks (GANs), can help in learning richer representations of the audio data. These models can generate realistic audio samples, which can be used to augment the training data and improve the encoder's performance.
Temporal Contextualization: Implementing techniques that focus on temporal contextualization, such as temporal convolutional networks (TCNs) or recurrent neural networks (RNNs), can help the encoder capture long-range dependencies in speech. This is particularly important in multi-speaker scenarios where the timing of speech overlaps is crucial.
By exploring these self-supervised learning techniques, the encoder's representation in the SOT-based multi-speaker ASR framework can be significantly improved, leading to better performance in recognizing overlapping speech.