asya: Mindful verbal communication using deep learning
This work addresses improving verbal communication for applications such as customer service and therapy, but it appears incremental as it applies existing deep learning methods to voice analysis.
The paper tackles the problem of analyzing human voice for various tasks like noise detection and emotion classification using deep learning, achieving over 95% accuracy in speaker diarization on test data.
asya is a mobile application that consists of deep learning models which analyze spectra of a human voice and do noise detection, speaker diarization, gender detection, tempo estimation, and classification of emotions using only voice. All models are language agnostic and capable of running in real-time. Our speaker diarization models have accuracy over 95% on the test data set. These models can be applied for a variety of areas like customer service improvement, sales effective conversations, psychology and couples therapy.