[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-31231":3,"doc-seo-31231":26},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"file_id":15,"file_url":16,"file_type":17,"file_size":18,"view_count":4,"is_deleted":4,"is_public":19,"is_downloadable":19,"audit_status":19,"page_count":11,"language":20,"language_code":21,"table_of_contents":22,"faqs":23,"seo_title":13,"seo_description":14,"update_tm":24,"read_time":25},31231,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","TinyML: On-Device Customization of Tiny Deep Learning Models for Keyword Spotting With Few Examples","The article proposes on-device customization for tiny keyword spotting (KWS) systems by initializing a DNN-based classifier using only a few recorded target command examples. A DNN feature extractor is trained offline with triplet loss to produce compact embeddings; during runtime, the classifier compares the current embedding to class prototypes computed on the device. Experiments on Google Speech Commands show up to 80% accuracy using just 10 unseen utterances, and latency of 9.7 ms on a 25 mW multicore microcontroller, enabling fast customization without backpropagation transfer learning.","cbCaioebjXOYOLzT","https://ap.wps.com/l/cbCaioebjXOYOLzT","pdf",897555,1,"English","en","# TinyML keyword spotting with few-shot customization\n## Motivation and problem setting on energy-limited MCUs\n## Proposed approach using triplet-loss feature embeddings and prototypes\n## Experimental evaluation on Google Speech Commands and on-device deployment","[{\"question\":\"What problem does the article address in TinyML keyword spotting?\",\"answer\":\"State-of-the-art TinyML KWS designs require time-consuming data collection and server-side training for each target scenario, creating a bottleneck for rapid prototyping on new devices and environments.\"},{\"question\":\"How does on-device customization work in the proposed method?\",\"answer\":\"A feature extractor is trained offline to output embeddings, then on-device class prototypes are computed from a few recorded examples; at inference, keyword classification is performed by measuring distance between the current embedding and prototypes.\"},{\"question\":\"What performance results are reported on the Google Speech Command dataset?\",\"answer\":\"Using 10 examples, the ResNet15-based model achieves up to 80% accuracy at a 5% false acceptance rate, with results reported as lossless under 8-bit quantization.\"}]",1779224472,20,{"code":4,"msg":27,"data":28},"ok",{"site_id":29,"language":21,"slug":30,"title":13,"keywords":31,"description":14,"schema_data":32,"social_meta":83,"head_meta":85,"extra_data":87,"updated_unix":24},105,"tinyml-on-device-customization-of-tiny-deep-learning-models-for-keyword-spotting-with-few-examples","",{"@graph":33,"@context":82},[34,51,65],{"@type":35,"itemListElement":36},"BreadcrumbList",[37,41,45,48],{"item":38,"name":39,"@type":40,"position":19},"https://docshare.wps.com","Home","ListItem",{"item":42,"name":43,"@type":40,"position":44},"https://docshare.wps.com/document/","Document",2,{"item":46,"name":12,"@type":40,"position":47},"https://docshare.wps.com/document/research-report/",3,{"item":49,"name":13,"@type":40,"position":50},"https://docshare.wps.com/document/tinyml-on-device-customization-of-tiny-deep-learning-models-for-keyword-spotting-with-few-examples/31231/",4,{"url":49,"name":13,"@type":52,"author":53,"headline":13,"publisher":55,"fileFormat":58,"description":14,"dateModified":59,"datePublished":59,"encodingFormat":58,"isAccessibleForFree":60,"interactionStatistic":61},"DigitalDocument",{"name":9,"@type":54},"Person",{"url":38,"name":56,"@type":57},"DocShare","Organization","application/pdf","2026-05-19",true,{"@type":62,"interactionType":63,"userInteractionCount":4},"InteractionCounter",{"@type":64},"ViewAction",{"@type":66,"mainEntity":67},"FAQPage",[68,74,78],{"name":69,"@type":70,"acceptedAnswer":71},"What problem does the article address in TinyML keyword spotting?","Question",{"text":72,"@type":73},"State-of-the-art TinyML KWS designs require time-consuming data collection and server-side training for each target scenario, creating a bottleneck for rapid prototyping on new devices and environments.","Answer",{"name":75,"@type":70,"acceptedAnswer":76},"How does on-device customization work in the proposed method?",{"text":77,"@type":73},"A feature extractor is trained offline to output embeddings, then on-device class prototypes are computed from a few recorded examples; at inference, keyword classification is performed by measuring distance between the current embedding and prototypes.",{"name":79,"@type":70,"acceptedAnswer":80},"What performance results are reported on the Google Speech Command dataset?",{"text":81,"@type":73},"Using 10 examples, the ResNet15-based model achieves up to 80% accuracy at a 5% false acceptance rate, with results reported as lossless under 8-bit quantization.","https://schema.org",{"og:url":49,"og:type":84,"og:title":13,"og:site_name":56,"og:description":14},"article",{"robots":86,"canonical":49},"index,follow",{"doc_id":7,"site_id":29}]