This post is divided into three parts; they are: • Understanding Text Embeddings • Other Techniques to Generate Embedding • How to Get a High-Quality Text Embedding? Text embeddings are to use numerical vectors to represent text.
Large Language Models (LLMs) can be adapted to extend their text capabilities to speech inputs. However, these speech-adapted LLMs consistently underperform their text-based counterparts—and even cascaded pipelines—on language understanding tasks. We term this shortfall the text-speech understanding gap: the performance drop observed when a speech-adapted LLM processes spoken inputs relative…