Learning Efficient Representations for Keyword Spotting with Triplet Loss

SPECOM 2021  ·  Roman Vygon, Nikolay Mikhaylovskiy ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Keyword Spotting Google Speech Commands TripletLoss-res15 Google Speech Commands V1 12 98.56 # 1
Google Speech Commands V2 12 98.37 # 5
Google Speech Commands V2 35 97.0 # 10


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