Papers › Learning Efficient Representations for Keyword Spotting with Triplet Loss
Learning Efficient Representations for Keyword Spotting with Triplet Loss
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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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Keyword Spotting | Google Speech Commands | TripletLoss-res15 | Google Speech Commands V1 12 | 98.56 | #1 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | TripletLoss-res15 | Google Speech Commands V2 12 | 98.37 | #1 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | TripletLoss-res15 | Google Speech Commands V2 35 | 97.0 | #1 of 42 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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