Sparse Autoencoder Insights on Voice Embeddings
This extends explainable ML techniques to audio domains like speaker recognition, though it's an incremental application of existing methods to new data.
The study applied sparse autoencoders to speaker embeddings from a Titanet model, showing they can extract mono-semantic features like language and music from non-textual data, with features exhibiting splitting and steering similar to those in LLM embeddings.
Recent advances in explainable machine learning have highlighted the potential of sparse autoencoders in uncovering mono-semantic features in densely encoded embeddings. While most research has focused on Large Language Model (LLM) embeddings, the applicability of this technique to other domains remains largely unexplored. This study applies sparse autoencoders to speaker embeddings generated from a Titanet model, demonstrating the effectiveness of this technique in extracting mono-semantic features from non-textual embedded data. The results show that the extracted features exhibit characteristics similar to those found in LLM embeddings, including feature splitting and steering. The analysis reveals that the autoencoder can identify and manipulate features such as language and music, which are not evident in the original embedding. The findings suggest that sparse autoencoders can be a valuable tool for understanding and interpreting embedded data in many domains, including audio-based speaker recognition.