Autodecompose: A generative self-supervised model for semantic decomposition

TL;DR

Autodecompose is a self-supervised generative model that disentangles data into semantic properties like sound source and content, enabling high-accuracy speaker recognition with minimal labeled data.

Contribution

It introduces a novel self-supervised approach for semantic decomposition using dual augmentations and encoders, without requiring labels, and demonstrates superior performance in audio source recognition.

Findings

Achieves 97.6% F1 in speaker recognition with only 10 seconds of labeled data.

Pre-trained on small datasets, it surpasses supervised models and resists overfitting.

Embeds content information separately, ignoring sound source in the context encoder.

Abstract

We introduce Autodecompose, a novel self-supervised generative model that decomposes data into two semantically independent properties: the desired property, which captures a specific aspect of the data (e.g. the voice in an audio signal), and the context property, which aggregates all other information (e.g. the content of the audio signal), without any labels given. Autodecompose uses two complementary augmentations, one that manipulates the context while preserving the desired property and the other that manipulates the desired property while preserving the context. The augmented variants of the data are encoded by two encoders and reconstructed by a decoder. We prove that one of the encoders embeds the desired property while the other embeds the context property. We apply Autodecompose to audio signals to encode sound source (human voice) and content. We pre-trained the model on YouTube and LibriSpeech datasets and fine-tuned in a self-supervised manner without exposing the labels. Our results showed that, using the sound source encoder of pre-trained Autodecompose, a linear classifier achieves F1 score of 97.6\% in recognizing the voice of 30 speakers using only 10 seconds of labeled samples, compared to 95.7\% for supervised models. Additionally, our experiments showed that Autodecompose is robust against overfitting even when a large model is pre-trained on a small dataset. A large Autodecompose model was pre-trained from scratch on 60 seconds of audio from 3 speakers achieved over 98.5\% F1 score in recognizing those three speakers in other unseen utterances. We finally show that the context encoder embeds information about the content of the speech and ignores the sound source information. Our sample code for training the model, as well as examples for using the pre-trained models are available here: \url{https://github.com/rezabonyadi/autodecompose}