Towards Solving Cocktail-Party: The First Method to Build a Realistic Dataset with Ground Truths for Speech Separation
Speech separation is very important in real-world applications such as
human-machine interaction, hearing aids devices, and automatic meeting
transcription. In recent years, a significant improvement occurred towards the
solution based on deep learning. In fact, much attention has been drawn to
supervised learning methods using synthetic mixtures datasets despite their
being not representative of real-world mixtures. The difficulty in building a
realistic dataset led researchers to use unsupervised learning methods, because
of their ability to handle realistic mixtures directly. The results of
unsupervised learning methods are still unconvincing. In this paper, a method
is introduced to create a realistic dataset with ground truth sources for
speech separation. The main challenge in designing a realistic dataset is the
unavailability of ground truths for speakers signals. To address this, we
propose a method for simultaneously recording two speakers and obtaining the
ground truth for each. We present a methodology for benchmarking our realistic
dataset using a deep learning model based on Bidirectional Gated Recurrent
Units (BGRU) and clustering algorithm. The experiments show that our proposed
dataset improved SI-SDR (Scale Invariant Signal to Distortion Ratio) by 1.65 dB
and PESQ (Perceptual Evaluation of Speech Quality) by approximately 0.5. We
also evaluated the effectiveness of our method at different distances between
the microphone and the speakers and found that it improved the stability of the
learned model.