Joint Speech Text

Joint speech-text modeling aims to create unified representations of speech and text, leveraging the strengths of both modalities to improve various tasks. Current research focuses on developing large language models (LLMs) that directly process speech and text, often employing techniques like multi-modal pre-training and counterfactual learning to enhance model robustness and understanding of nuanced contexts. This approach shows promise in improving speech recognition, language understanding, and text-to-speech applications, particularly in low-resource scenarios and tasks requiring fine-grained control over generated speech. The resulting advancements have significant implications for human-computer interaction, accessibility technologies, and the broader field of artificial intelligence.