Interface Design for Self-Supervised Speech Models
Yi-Jen Shih, David Harwath
2024-06-20
Summary
This paper discusses how to improve the way we use self-supervised speech models by designing better ways to connect their different layers to specific tasks, making them more effective for speech processing.
What's the problem?
Self-supervised speech models are popular for tasks like speech recognition, but they often use a simple method to combine information from different layers of the model. This method, called a layerwise weighted sum, may not work well for all tasks. As a result, we miss out on the full potential of these models because we aren't using the best ways to connect their features to the specific tasks we want them to perform.
What's the solution?
The researchers propose new interface designs that link the different layers of self-supervised speech models to downstream tasks. They show that using a convolutional interface, which scales efficiently with the model's depth, performs better than the traditional weighted sum method. This means that by changing how we connect model layers to tasks, we can achieve better results in speech processing applications.
Why it matters?
Improving how we design interfaces for self-supervised speech models is important because it can enhance their performance across various tasks. This could lead to more accurate and efficient speech recognition systems, which are essential for applications like virtual assistants, transcription services, and other technologies that rely on understanding spoken language.
Abstract
Self-supervised speech (SSL) models have recently become widely adopted for many downstream speech processing tasks. The general usage pattern is to employ SSL models as feature extractors, and then train a downstream prediction head to solve a specific task. However, different layers of SSL models have been shown to capture different types of information, and the methods of combining them are not well studied. To this end, we extend the general framework for SSL model utilization by proposing the interface that connects the upstream and downstream. Under this view, the dominant technique of combining features via a layerwise weighted sum can be regarded as a specific interface. We propose several alternative interface designs and demonstrate that the weighted sum interface is suboptimal for many tasks. In particular, we show that a convolutional interface whose depth scales logarithmically with the depth of the upstream model consistently outperforms many other interface designs.