# Towards Dog Bark Decoding: Leveraging Human Speech Processing for Automated Bark Classification

arXiv: https://arxiv.org/abs/2404.18739 · HTML: https://mlsanity.com/paper/2404.18739/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts](https://mlsanity.com/paper/2009.10132/) (2020)
- [Compositional Demographic Word Embeddings](https://mlsanity.com/paper/2010.02986/) (2020)
- [AutoSpeech: Neural Architecture Search for Speaker Recognition](https://mlsanity.com/paper/2005.03215/) (2020)
- [Estimation of speaker age and height from speech signal using bi-encoder transformer mixture model](https://mlsanity.com/paper/2203.11774/) (2022)
- [The Orchive : Data mining a massive bioacoustic archive](https://mlsanity.com/paper/1307.0589/) (2013)

## Built on by (most cited first, 6 of 6)

- [Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing](https://mlsanity.com/paper/2501.05987/) (2025)
- [Speciesism in Natural Language Processing Research](https://mlsanity.com/paper/2410.14194/) (2024)
- [Towards Leveraging Sequential Structure in Animal Vocalizations](https://mlsanity.com/paper/2511.10190/) (2025)
- [GmSLM : Generative Marmoset Spoken Language Modeling](https://mlsanity.com/paper/2509.09198/) (2025)
- [Beyond Discrete Categories: Multi-Task Valence-Arousal Modeling for Pet Vocalization Analysis](https://mlsanity.com/paper/2510.12819/) (2025)
- [CREMD: Crowd-Sourced Emotional Multimodal Dogs Dataset](https://mlsanity.com/paper/2602.15349/) (2026)

## Abstract (arXiv)

Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered tasks in speech recognition. We specifically address four tasks: dog recognition, breed identification, gender classification, and context grounding. We show that using speech embedding representations significantly improves over simpler classification baselines. Further, we also find that models pre-trained on large human speech acoustics can provide additional performance boosts on several tasks.