Revisiting Speech Content Privacy

TL;DR

This paper explores the importance of speech content privacy, discussing various scenarios, privacy types, and evaluation strategies to enhance protection of spoken content using machine learning advancements.

Contribution

It provides a comprehensive discussion on speech content privacy, introduces different privacy types, and proposes evaluation strategies, addressing an under-explored area in speech technology.

Findings

Identifies multiple scenarios requiring speech content privacy

Classifies content privacy into recoverable and non-recoverable types

Proposes evaluation strategies and discusses associated challenges

Abstract

In this paper, we discuss an important aspect of speech privacy: protecting spoken content. New capabilities from the field of machine learning provide a unique and timely opportunity to revisit speech content protection. There are many different applications of content privacy, even though this area has been under-explored in speech technology research. This paper presents several scenarios that indicate a need for speech content privacy even as the specific techniques to achieve content privacy may necessarily vary. Our discussion includes several different types of content privacy including recoverable and non-recoverable content. Finally, we introduce evaluation strategies as well as describe some of the difficulties that may be encountered.