Ab­stract

Key­word spot­ting (KWS) is be­com­ing a ubiq­ui­tous need with the ad­vance­ment in ar­ti­fi­cial in­tel­li­gence and smart de­vices. Re­cent work in this field have fo­cused on sev­eral dif­fer­ent ar­chi­tec­tures to achieve good re­sults on datasets with low to mod­er­ate noise. How­ever, the per­for­mance of these mod­els de­te­ri­o­rates under high noise con­di­tions as shown by our ex­per­i­ments. In our paper, we pre­sent an ex­ten­sive com­par­i­son be­tween state-of-the-art KWS net­works under var­i­ous noisy con­di­tions. We also sug­gest adap­tive batch nor­mal­iza­tion as a tech­nique to im­prove the per­for­mance of the net­works when the noise files are un­known dur­ing the train­ing phase. The re­sults of such high noise char­ac­ter­i­za­tion en­able fu­ture work in de­vel­op­ing mod­els that per­form bet­ter in the afore­men­tioned con­di­tions.