Computer Science > Sound
arXiv:2210.14252 (cs)
[Submitted on 25 Oct 2022]
Title:Dynamic Speech Endpoint Detection with Regression Targets
Authors:Dawei Liang,Hang Su,Tarun Singh,Jay Mahadeokar,Shanil Puri,Jiedan Zhu,Edison Thomaz,Mike Seltzer
View a PDF of the paper titled Dynamic Speech Endpoint Detection with Regression Targets, by Dawei Liang and 7 other authors
View PDFAbstract:Interactive voice assistants have been widely used as input interfaces in various scenarios, e.g. on smart homes devices, wearables and on AR devices. Detecting the end of a speech query, i.e. speech end-pointing, is an important task for voice assistants to interact with users. Traditionally, speech end-pointing is based on pure classification methods along with arbitrary binary targets. In this paper, we propose a novel regression-based speech end-pointing model, which enables an end-pointer to adjust its detection behavior based on context of user queries. Specifically, we present a pause modeling method and show its effectiveness for dynamic end-pointing. Based on our experiments with vendor-collected smartphone and wearables speech queries, our strategy shows a better trade-off between endpointing latency and accuracy, compared to the traditional classification-based method. We further discuss the benefits of this model and generalization of the framework in the paper.
| Comments: | Manuscript submitted to ICASSP 2023 |
| Subjects: | Sound (cs.SD); Audio and Speech Processing (eess.AS) |
| Cite as: | arXiv:2210.14252 [cs.SD] |
| (orarXiv:2210.14252v1 [cs.SD] for this version) | |
| https://doi.org/10.48550/arXiv.2210.14252 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Dynamic Speech Endpoint Detection with Regression Targets, by Dawei Liang and 7 other authors
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