Talkatail

team, mid-grind

Inspiration

Being pet parents, we were always intrigued by what goes inside our dog's mind. Most of the times it can be hard to understand what our pets are feeling or when something’s wrong, often health or mood changes are noticed very late, thus, we wanted to truly listen to our pets and have a solution which will help other pet lovers like us.

What it does

Talkatail uses AI to detect, interpret and predict a pet’s behavior and health from sounds, body vitals and patterns. It is a one stop solution for pet lovers to closely monitor their pet's wellbeing.

How we built it

Talkatail is a solution which integrates two custom-made models-BarkOTell and health analyser to give a deep analysis of your pet’s well which will be showcased on an application. BarkOtell is a deep learning–powered system that translates dog vocalizations into human-understandable emotions, bridging the communication gap between humans and their pets. By converting audio recordings of barks, howls, and whimpers into spectrograms, the project treats sound classification as an image recognition problem and trains a convolutional neural network on Azure Machine Learning for efficient, GPU-accelerated training. The resulting model classifies emotions such as Excited, Anxious, or Warning and is deployed through a Flask-based web app with a user-friendly interface for real-time recording or audio upload.
The Health Analyzer is a model which utilizes generated synthetic health metrics and derives insights from it using a scaler for normalization and machine learning- logistic regression. Despite challenges in dataset collection and environment integration, Talkatail successfully delivers an end-to-end ML pipeline that showcases advanced data processing, model training, and full-stack deployment, paving the way for future features like integration with smart assistants and two-way pet communication.

Challenges we ran into

The first challenge was of scoping the solution for hackathon development and second was of collecting and accurately labeling diverse, high-quality pet sound.

Accomplishments that we're proud of

1.⁠ ⁠We are proud of working on a first of its kind idea and building a working prototype that can interpret and summarize pet mood and health.

We are also proud of developing a model whose input and processed data is of different modalities.
We are also satisfied with achieving a decent accuracy in emotion classification using limited training data. We are happy to have created a pet monitoring experience that feels empathetic and human-centered.

What we learned

1.⁠ ⁠The importance of explainable AI when dealing with human–animal communication. 2.⁠ ⁠How IoT and NLP can work together to make everyday interactions smarter and more natural. 3.⁠ ⁠The challenges of real-time data processing from multiple sensors. 4.⁠ ⁠Empathy-driven technology can strengthen the human–pet bond.

What's next for Talkatail

1.⁠ ⁠Expanding beyond dogs to include cats and other pets. 2.⁠ ⁠Partnering with veterinarians for preventive health insights. 3.⁠ ⁠Adding emotion-based training recommendations. 4.⁠ ⁠Launching a mobile app and voice assistant for on-the-go monitoring.