2018/12/19 by Raphael Tang, Gefei Yang, Tang, Raphael +10
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Music and Audio Processing #Speech Recognition and Synthesis #Speech and Audio Processing #cs.CL
paper · pdf · doi:10.48550/arxiv.1812.07754
5 pages, 2 figures, submitted to ICASSP 2019
arxiv created 2018/12/19 · openalex publication_date 2018/12/19 · arxiv updated 2018/12/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Voice-enabled commercial products are ubiquitous, typically enabled by lightweight on-device keyword spotting (KWS) and full automatic speech recognition (ASR) in the cloud. ASR systems require significant computational resources in training and for inference, not to mention copious amounts of annotated speech data. KWS systems, on the other hand, are less resource-intensive but have limited capabilities. On the Comcast Xfinity X1 entertainment platform, we explore a middle ground between ASR and KWS: We introduce a novel, resource-efficient neural network for voice query recognition that is much more accurate than state-of-the-art CNNs for KWS, yet can be easily trained and deployed with limited resources. On an evaluation dataset representing the top 200 voice queries, we achieve a low false alarm rate of 1% and a query error rate of 6%. Our model performs inference 8.24x faster than the current ASR system.