Learning Efficient Representations for Keyword Spotting with Triplet Loss

Abstract

In the past few years, triplet loss-based metric embeddings have become a de-facto standard for several important computer vision problems, most no-tably, person reidentification. On the other hand, in the area of speech recognition the metric embeddings generated by the triplet loss are rarely used even for classification problems. We fill this gap showing that a combination of two representation learning techniques: a triplet loss-based embedding and a variant of kNN for classification instead of cross-entropy loss significantly (by 26% to 38%) improves the classification accuracy for convolutional networks on a LibriSpeech-derived LibriWords datasets. To do so, we propose a novel phonetic similarity based triplet mining approach. We also improve the current best published SOTA for Google Speech Commands dataset V1 10+2 -class classification by about 34%, achieving 98.55% accuracy, V2 10+2-class classification by about 20%, achieving 98.37% accuracy, and V2 35-class classification by over 50%, achieving 97.0% accuracy.

Submitted to SPECOM 2021

References in corpus (3)

• Towards Learning a Universal Non-Semantic Representation of Speech
• Streaming keyword spotting on mobile devices
• Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting

Cited by in corpus (6)

• Keyword Transformer: A Self-Attention Model for Keyword Spotting
• Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors
• Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition
• On a time-frequency blurring operator with applications in data augmentation
• AUC Optimization for Robust Small-footprint Keyword Spotting with Limited Training Data
• Attention-Free Keyword Spotting