Transcribing Vocal Communications of Domestic Shiba lnu Dogs (2023)

Abstract: How animals communicate and whether they have languages is a persistent curiosity of human beings. However, the study of animal communications has been largely restricted to data from field recordings or in a controlled environment, which is expensive and limited in scale and variety. In this paper, we take domestic Shiba Inu dogs as an example, and extract their vocal communications from large amount of YouTube videos of Shiba Inu dogs. We classify these clips into different scenarios and locations, and further transcribe the audio into phonetically symbolic scripts through a systematic process. We discover consistent phonetic symbols among their expressions, which indicates that Shiba Inu dogs can have systematic verbal communication patterns. This reusable framework produces the first-of-its-kind Shiba Inu vocal communication dataset that will be valuable to future research in both zoology and linguistics.

## introduction it has long been an interesting interdisciplinary scientific challenge to understand the languages of animals @xcite @xcite . dogs, who are arguably the best friends of humans, have drawn particular attention. learning what dogs want to express has broad and profound significance, such as in understanding biological evolution @xcite , for applying their languages to information technology, or sometimes just for satisfying the our curiosity. vocal expressions of dogs, being their chief means of communication, have been studied previously. here we define vocal expressions as all the sounds that a dog can make vocally, including bark, whine, whimper, howl, huff, growl, yelp, and yip. it has been shown that dogs can recognize the scenes and express their understandings of the outer world as well as their inner states by their voices @xcite . the limitations of previous works are from two aspects. on the one hand, previous research treats this task as a simple classification problem, which means that an audio segment containing barks will be straightforwardly sent into one model to get a particular label such as emotion (happy or sad). although the results of them have shown that dogs have consistent sound patterns for different purposes, they provided little insight into exploring whether dogs have structural languages. the potential linguistic patterns beyond the dog's vocal expressions are dramatically ignored. on the other hand, previous datasets are collected by recording the voices of dogs under certain controlled environments. such methodology is costly in practice, and the data thus produced is limited in size and variety (as we will show later in figure 1 : we aim at matching the barks of dogs with its semantic meanings. in our approach, the barks of dogs will be transcribed into symbols. even though it is still highly debatable whether animals, or dogs in this case, have languages at all, in this paper, we present an approach pipeline to treat dog sounds as a kind of language, similar to human languages. during this process (figure 1 ), the specific patterns found in their vocal expressions imply that their barking sounds can carry corresponding semantic meanings just as humans use fixed sound patterns to signify. in this paper, we present a dataset of phonetically symbolic transcripts of shiba inu dog barks foot_0 called shibascript, which ameliorates some of the aforementioned challenges. we pick shiba inu as the subject because it is a popular breed around the world and there are a large number of their videos on the web. meanwhile, we provide preliminary phonetic analysis on this dataset. we believe that this work is a first step toward investigating whether dogs have sound-actuated language just as humans with speech. shibascript contains barks coming from 16 different shiba inu dogs, corresponding transcripts with timestamps of their barks, among which consistent sound patterns are found. these 16 dogs are respectively from 16 families who post these dogs' videos on youtube. the dataset has a total length of over 4 hours of pure dog sound production, 4469 sentences, and 7761 words. there are in total 9 distinct syllables in these transcripts. note that due to the ever-evolving nature of social media, the dataset-construction methodology we propose in this paper can be applied to youtube continuously and yields a dataset that is growing in size and variety. we believe this dataset will help with future research on canine communication as well as any general audience who are interested in learning what dogs want to express. our contributions lie in three aspects: ## approach we now describe the method of constructing shibascript. to collect these clean shiba inu barks and endow them with corresponding transcripts, a six-step process is used. these steps, in sequence, are getting videos related to shiba inu dogs, extracting barks as "sentences" removing barks with noise, extracting barks as "words", separating syllables, and clustering to assign appropriate phonemes based on their acoustic features. ## analysis we present preliminary statistical findings from shibascript, including lexical analysis and transcribing accuracy evaluation. ## conclusion in this work, we introduce an unprecedented approach for transcribing vocal communications of shiba inu dogs and release a corresponding dataset shibascript. compared to the former approaches, it can save a lot of cost and make the dataset extensible. the approach can be transferred to other animals easily. and most importantly, the method is the first-step into investigating the vocal patterns of dogs, bringing inspiration to the field of animal understanding. we also make some preliminary statistical evaluation and analysis on shibascript. the evaluation shows that our symbol assignments in those transcripts are consistent. in the analysis part, we have shown some interesting findings related to the lexical distribution. for future work, we can further research the semantic meanings of dog vocal expressions because we have obtained the corresponding videos of dog vocal expressions.