  Supervised speech enhancement relies on parallel databases of degraded speech
signals and their clean reference signals during training. This setting
prohibits the use of real-world degraded speech data that may better represent
the scenarios where such systems are used. In this paper, we explore methods
that enable supervised speech enhancement systems to train on real-world
degraded speech data. Specifically, we propose a semi-supervised approach for
speech enhancement in which we first train a modified vector-quantized
variational autoencoder that solves a source separation task. We then use this
trained autoencoder to further train an enhancement network using real-world
noisy speech data by computing a triplet-based unsupervised loss function.
Experiments show promising results for incorporating real-world data in
training speech enhancement systems.
