Data-Centric Lessons To Improve Speech-Language Pretraining

This article from Apple ML highlights the importance of data-centric approaches in improving Speech-Language Models (SpeechLMs) for Spoken Question-Answering (SQA). It points out the lack of controlled studies on pretraining data processing and curation, hindering a clear understanding of performance factors. The research aims to address this gap by exploring data-centric methods for pretraining SpeechLMs. The focus on data-centric exploration suggests a shift towards optimizing the quality and selection of training data to enhance model performance, rather than solely focusing on model architecture.

• •Data-centric approaches are crucial for improving SpeechLMs.
• •Lack of controlled studies on data processing hinders understanding of performance.
• •The research aims to explore data-centric methods for pretraining SpeechLMs.