Statistical automatic species identification of microchiroptera from echolocation calls: Lessons learned from human automatic speech recognition
Current automatic species identification of microchiroptera through echolocation search calls utilizes holistic features, such as call duration, bandwidth, and frequency extrema, which stem from expert knowledge of sonograms of individual calls. This approach parallels the early acoustic‐phonetic paradigm of human automatic speech recognition (ASR), which relied heavily on expert knowledge to account for variations in canonical linguistic units of interest. Acoustic‐phonetic ASR gave way to the statistical paradigm of ASR primarily because of the superior ability of machine learning to account for variations in the acoustic signal (noise, speaker characteristics) [Juang and Furui, Proc. IEEE 88 (8), 1142–1165 (2000)]. In the current work, machine learning methods from human ASR, hidden Markov models (HMM) and dynamic time warping (DTW), are considered for the problem of automatic species identification of microchiroptera from echolocation calls. The extensive history of ASR provides valuable lessons, whic...