4 accepted papers
A vastly under-explored area in speech anonymization involves characterizing how different speakers perform in voice privacy tasks. In this paper, we present a deeper analysis by creating and analyzing groups of challenging speakers categorized based on their performance in two related facets of voi…
In this paper, we introduce an audio-based framework for occupancy estimation, including a new public dataset, and evaluate occupancy in a ‘cocktail party’ scenario where the party is simulated by mixing audio to produce speech with overlapping talkers (1-10 people). To estimate the number of speake…
We present a new approach to disentangle speaker voice and phone content by introducing new components to the VQ-VAE architecture for speech synthesis. The original VQ-VAE does not generalize well to unseen speakers or content. To alleviate this problem, we have incorporated a speaker encoder and sp…
Preliminary experiments in this dissertation show that it is possible to factorize specific types of information from the speech signal in an abstract embedding space using machine learning. This information includes characteristics of the recording environment, speaking style, and speech quality. B…