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 per-ception of speech has guided the design of many useful algorithms, and automatic speech recognition has been at the forefront of the ma-chine learning paradigm. In contrast to the advances made in auto-matic 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 ra-tio of an audible monitoring signal was improved by 12 dB using techniques from noise-robust feature extraction and speech synthe-sis. The work demonstrates the impact that speech research can have across disciplines. 1