Phonetic and Lexical Discovery of Canine Vocalization (2024)

Abstract: This paper attempts to discover communication patterns automatically within dog vocalizations in a data-driven approach, which breaks the barrier previous approaches that rely on human prior knowledge on limited data. We present a self-supervised approach with HuBERT, enabling the accurate classification of phones, and an adaptive grammar induction method that identifies phone sequence patterns that suggest a preliminary vocabulary within dog vocalizations. Our results show that a subset of this vocabulary has substantial causality relations with certain canine activities, suggesting signs of stable semantics associated with these “words”.

## introduction the concept of "animal language" hinges on the intricate ways through which non-human species communicate, revealing a spectrum of vocalizations, gestures, and behavioral cues that resemble humans' capabilities to convey information and emotions. the study of animal communication has captivated researchers across numerous disciplines, from biology to linguistics @xcite @xcite . while it is commonly accepted that most animals lack a language system comparable to human languages, recent advancements in natural language processing have opened up new avenues for investigating the patterns and structures embedded within animal vocalizations @xcite @xcite . the study of animal language abilities, therefore, not only broadens our understanding of the animal kingdom but also deepens our insights into the evolution and functions of communication systems across species. despite observable acoustic variations in canine vocalizations that hint at potential patterns of com-munication, asserting that dogs possess a language is fraught with limitations. the absence of identifiable phones and structured syntax in their vocalizations challenges traditional linguistic frameworks @xcite . while dogs exhibit a range of sounds that vary in pitch, duration, and intensity, as illustrated in figure 1 (sound clips extracted from audioset @xcite ), six distinct dog barking sounds are identified. however, these variations alone do not constitute a language. the lack of a structured, consistent phonetic system and the inability to form complex ideas through their sounds significantly limit the comparison of their vocalizations to human language. nevertheless, undertaking the challenge of exploring the concept of animal language is a daunting task. unlike well-researched human language, the frequency range and phonetic variations remain underexplored, rendering the classification approach based on sound amplitude inadequate for discerning the fundamental phones of dog vocalizations. the difficulty lies not only in interpreting the acoustic variations but also in identifying meaningful patterns within the vast array of animal sounds. this complexity is compounded by the need to distinguish between mere noise and significant vocalizations that could indicate some form of structured communication. the endeavor requires innovative methodologies and a departure from traditional linguistic analysis. our approach to navigating these challenges involves leveraging advanced signal processing techniques and self-supervised learning models. by focusing on the acoustic features of dog vocalizations, we aim to uncover underlying patterns that suggest an elementary form of phonetics. this involves a systematic process of audio cleanup, sentence extraction, phone recognition, and combination across vocalizations from different individual dogs. given the lack of prior knowledge of dog vocalizations, we apply a self-supervised approach, hubert @xcite , for representation pretraining and phone identification. hubert can effectively reference the contextual information of the audio and generate vector representations, which provides robustness when faced with vocalizations that have slight variations in context. utilizing the precise sequences of phones acquired from various dog vocalizations, we explored the feasibility of creating a vocabulary of frequently occurring subsequences of phones, which we call "words" here 1 . we identify these words by applying an adaptor grammar induction algorithm @xcite on a simple but universal syntax. we assess the validity of a word by evaluating the statistical correlation between its occurrence and the dog activities before, during, and after the utterance of the word, which serves as an indication of consistent lexical semantics. our analysis revealed that a sizable portion of the vocabulary possesses semantics. our contributions lie in three aspects: ## data collection and segmentation in this section, we detail our workflow foot_0 (figure 2 ), including audio cleanup by audiosep, sentence extraction, dataset preparation, phone recognition, word discovery, and semantics discovery. the first three parts are primarily intended to gather large amounts of high-quality data on dog barking, while the last three parts aim to investigate potential phones and words in dog vocalizations. ## phonetic and lexical discovery in this section, we detail our approach in training a model to identify canine phones from the above segmented data and discover possible semantic units. ## evaluation in order to investigate: (1) whether the phones after dog-hubert are distinct and accurate, and (2) whether the words discovered are complete and semantically consistent, we evaluate our models' performances on phone recognition accuracy and vocabulary discovery. ## related work to decode a dog's language, it is necessary to analyze its basic sound units, linguistic structure, lexicon, meaning, and more. the past few years have seen a surge of interest in using machine learning (ml) methods for studying the behavior of nonhuman animals @xcite . much of the past work has primarily focused on dog behavior @xcite @xcite and the meaning of dog sounds @xcite @xcite @xcite . most of these the above work illustrates the existence of multiple distinct sound units in dog language. many species that appear to use only a handful of basic call types may possess rich vocal repertoires @xcite . numerous studies demonstrate the diversity of animal sounds @xcite @xcite . @xcite and @xcite conducted fine-grained studies of dog sound units. however, using a priori knowledge of human language directly may not be applicable. @xcite and hagiwara (2023) also conducted fine-grained studies of dog sound units, but they did not explore the possible meanings of these units. conducting unsupervised lexicon discovery on speech without any prior knowledge is a very challenging task @xcite . @xcite achieved end-to-end human lexicon discovery from audio by jointly training hidden markov models (hmms) @xcite for phone discovery and adaptor grammar @xcite for lexicon discovery. however, the accuracy of their lexicon discovery remains low. in this paper, we use an improved approach to train the parameters for adaptor grammar. ## conclusion in this paper, we present a method to parse and understand canine vocalizations. in contrast to previous work, this approach uses a self-supervised method instead of relying on human language knowledge to explore sound units in canine language. this is better suited for examining finegrained phonetics and semantics in a language with no prior linguistic knowledge. we then use hybrid variational-mcmc inference to train the parameters of the adaptor grammar, ultimately obtaining a candidate vocabulary. by simultaneously considering dog activity events surrounding the vocalizations, we find that some words in the vocabulary have strong correlations with certain activities.