Learning Efficient Representations for Keyword Spotting with Triplet Loss

Understand

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.