Automatic Speech Processing Methods for Bioacoustic Signal Analysis: A Case Study of Cross-Disciplinary Acoustic Research

Automatic speech processing research has produced many advances in the analysis of time series. Knowledge of the production and perception of speech has guided the design of many useful algorithms, and automatic speech recognition has been at the forefront of the machine learning paradigm. In contrast to the advances made in automatic speech processing, analysis of other bioacoustic signals, such as those from dolphins and bats, has lagged behind. In this paper, we demonstrate how techniques from automatic speech processing can significantly impact bioacoustic analysis, using echolocating bats as our model animal. Compared to conventional techniques, machine learning methods reduced detection and species classification error rates by an order of magnitude. Furthermore, the signal-to-noise ratio of an audible monitoring signal was improved by 12 dB using techniques from noise-robust feature extraction and speech synthesis. The work demonstrates the impact that speech research can have across disciplines