Embedded Modules for Speaker Classification

Abstract

Classifying speakers and their context is a research topic that increasingly finds its way into market-ready products. This paper describes how a speech-based classification problem can be split into components that are then combined in a classification module, which can be compiled for a specific platform and scenario with its respective technical requirements and limitations. We are focusing on the AGENDER Speaker Classification approach to show how a theoretic model can be transformed into a finished embedded module and present a tool that facilitates this in a fully automated build process.