PB-LRDWWS System for the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting Challenge

TL;DR

The paper presents a prototype-based dysarthric speech recognition system using a fine-tuned HuBERT model, achieving second place in the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting Challenge.

Contribution

It introduces a novel combination of a fine-tuned HuBERT feature extractor with prototype-based classification for dysarthric speech recognition.

Findings

Achieved second place in the LRDWWS Challenge

Effective prototype-based classification for low-resource dysarthric speech

Demonstrated simplicity and effectiveness of the approach

Abstract

For the SLT 2024 Low-Resource Dysarthria Wake-Up Word Spotting (LRDWWS) Challenge, we introduce the PB-LRDWWS system. This system combines a dysarthric speech content feature extractor for prototype construction with a prototype-based classification method. The feature extractor is a fine-tuned HuBERT model obtained through a three-stage fine-tuning process using cross-entropy loss. This fine-tuned HuBERT extracts features from the target dysarthric speaker's enrollment speech to build prototypes. Classification is achieved by calculating the cosine similarity between the HuBERT features of the target dysarthric speaker's evaluation speech and prototypes. Despite its simplicity, our method demonstrates effectiveness through experimental results. Our system achieves second place in the final Test-B of the LRDWWS Challenge.