# Prototypical Metric Transfer Learning for Continuous Speech Keyword Spotting With Limited Training Data

## TL;DR

This paper introduces a novel transfer learning approach using prototypical and metric loss functions to improve continuous speech keyword spotting accuracy with limited training data, addressing class imbalance.

## Contribution

It proposes a new combination of loss functions and transfer learning techniques specifically designed for CSKS with scarce data, enhancing detection performance.

## Key findings

- F1 score improved by over 10%
- Effective handling of limited training data
- Addresses class imbalance in CSKS

## Abstract

Continuous Speech Keyword Spotting (CSKS) is the problem of spotting keywords in recorded conversations, when a small number of instances of keywords are available in training data. Unlike the more common Keyword Spotting, where an algorithm needs to detect lone keywords or short phrases like "Alexa", "Cortana", "Hi Alexa!", "Whatsup Octavia?" etc. in speech, CSKS needs to filter out embedded words from a continuous flow of speech, ie. spot "Anna" and "github" in "I know a developer named Anna who can look into this github issue." Apart from the issue of limited training data availability, CSKS is an extremely imbalanced classification problem. We address the limitations of simple keyword spotting baselines for both aforementioned challenges by using a novel combination of loss functions (Prototypical networks' loss and metric loss) and transfer learning. Our method improves F1 score by over 10%.