JOOCI: A FRAMEWORK FOR LEARNING COMPREHENSIVE SPEECH REPRESENTATIONS
Keywords: SSL, Speech Representation Learning, Joint Optimization
TL;DR: A framework capable of jointly learning comprehensive speech representations i.e., the content and other information.
Abstract: Information in speech can be divided into two categories: what is being said (content) and how it is expressed (other). Current state-of-the-art (SOTA) techniques model speech at fixed segments, usually 10-25 ms, using a single embedding. Given the orthogonal nature of other and content information, attempting to optimize both within a single embedding results in suboptimal solutions. This approach divides the model's capacity, limiting its ability to build complex hierarchical features effectively. In this work, we present an end-to-end speech representation learning framework designed to jointly optimize the other and \enquote{content} information (JOOCI) in speech. By using separate learnable parameters, JOOCI addresses this optimization challenge by modeling other and content information independently. Our results show that JOOCI consistently outperforms other SOTA models of similar size (100 million parameters) and pre-training data used (960 hours) by a significant margin when evaluated on a range of speech downstream tasks in the SUPERB benchmark. Code and models are available at TBA.
Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
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Submission Number: 13918
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