Paper ID | SPE-42.6 | ||
Paper Title | QUERY-BY-EXAMPLE KEYWORD SPOTTING SYSTEM USING MULTI-HEAD ATTENTION AND SOFTTRIPLE LOSS | ||
Authors | Jinmiao Huang, Waseem Gharbieh, Han Suk Shim, Eugene Kim, LG Electronics, Canada | ||
Session | SPE-42: Keyword Spotting | ||
Location | Gather.Town | ||
Session Time: | Thursday, 10 June, 15:30 - 16:15 | ||
Presentation Time: | Thursday, 10 June, 15:30 - 16:15 | ||
Presentation | Poster | ||
Topic | Speech Processing: [SPE-GASR] General Topics in Speech Recognition | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | This paper proposes a neural network architecture for tackling the query-by-example user-defined keyword spotting task. A multi-head attention module is added on top of a multi-layered GRU for effective feature extraction, and a normalized multi-head attention module is proposed for feature aggregation. We also adopt the softtriple loss - a combination of triplet loss and softmax loss - and showcase its effectiveness. We demonstrate the performance of our model on internal datasets with different languages and the public Hey-Snips dataset. We compare the performance of our model to a baseline system and conduct an ablation study to show the benefit of each component in our architecture. The proposed work shows solid performance while preserving simplicity. |