Rethinking Algorithm Design and Development in Speech Processing

Abstract

Speech processing is typically based on a set of complex algorithms requiring many parameters to be specified. When parts of the speech processing chain do not behave as expected, trial and error is often the only way to investigate the reasons. In this paper, we present a research methodology to analyze unexpected algorithmic behavior by making (intermediate) results of the speech processing chain perceivable and intuitively comprehensible by humans. The workflow of the process is explicated using a real-world example leading to considerable improvements in speaker clustering. The described methodology is supported by a software toolbox available for download.