The proposed multi-view pseudo-labeling approach can be effectively extended to various speech classification tasks by adapting the framework to leverage the unique characteristics of different domains. For instance, in tasks such as speaker identification, sentiment analysis, or even medical diagnosis from speech, the multi-view pseudo-labeling can utilize both acoustic and linguistic features tailored to the specific requirements of these tasks.
Task-Specific Feature Extraction: Different speech classification tasks may benefit from specialized feature extraction methods. For example, in speaker identification, the acoustic path could focus on speaker-specific voice characteristics, while the linguistic path could analyze the content of speech for contextual clues.
Diverse Audio Encoders: The framework can incorporate additional audio encoders that are optimized for the specific characteristics of the new tasks. For instance, using encoders trained on speaker recognition datasets can enhance the acoustic path for speaker identification tasks.
Linguistic Model Adaptation: The linguistic path can be adapted by fine-tuning large language models (LLMs) on domain-specific corpora. For instance, in medical diagnosis, LLMs can be trained on clinical notes or patient interactions to improve the accuracy of pseudo-labeling based on ASR transcriptions.
Integration of Additional Modalities: The framework can be expanded to include other modalities, such as visual data from lip movements or facial expressions, which can complement the acoustic and linguistic features, especially in tasks like sentiment analysis or affective computing.
Iterative Refinement: The iterative nature of the semi-supervised training can be maintained, allowing the model to continuously improve its performance as more unlabeled data becomes available, thus enhancing its applicability across various speech classification tasks.
By leveraging these strategies, the multi-view pseudo-labeling approach can be adapted to a wide range of speech classification tasks, enhancing its versatility and effectiveness.