ISPA: Inter-Species Phonetic Alphabet for Transcribing Animal Sounds
This provides a novel approach for bioacoustics researchers to analyze animal sounds using interpretable text transcriptions, though it is incremental in applying existing language models to a new domain.
The paper tackles the problem of transcribing animal sounds by introducing ISPA, a text-based system that enables the application of human language models, achieving performance comparable to traditional audio representation methods.
Traditionally, bioacoustics has relied on spectrograms and continuous, per-frame audio representations for the analysis of animal sounds, also serving as input to machine learning models. Meanwhile, the International Phonetic Alphabet (IPA) system has provided an interpretable, language-independent method for transcribing human speech sounds. In this paper, we introduce ISPA (Inter-Species Phonetic Alphabet), a precise, concise, and interpretable system designed for transcribing animal sounds into text. We compare acoustics-based and feature-based methods for transcribing and classifying animal sounds, demonstrating their comparable performance with baseline methods utilizing continuous, dense audio representations. By representing animal sounds with text, we effectively treat them as a "foreign language," and we show that established human language ML paradigms and models, such as language models, can be successfully applied to improve performance.