Multiple-Instance, Cascaded Classification for Keyword Spotting in Narrow-Band Audio

Autor:	AbdulKader, Ahmad, Nassar, Kareem, Mahmoud, Mohamed, Galvez, Daniel, Patil, Chetan
Rok vydání:	2017
Předmět:	Computer Science - Learning Computer Science - Computation and Language Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	We propose using cascaded classifiers for a keyword spotting (KWS) task on narrow-band (NB), 8kHz audio acquired in non-IID environments --- a more challenging task than most state-of-the-art KWS systems face. We present a model that incorporates Deep Neural Networks (DNNs), cascading, multiple-feature representations, and multiple-instance learning. The cascaded classifiers handle the task's class imbalance and reduce power consumption on computationally-constrained devices via early termination. The KWS system achieves a false negative rate of 6% at an hourly false positive rate of 0.75 Comment: To be published in the proceedings of NIPS 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1711.08058 Zobrazit plný text záznamu View this record from Arxiv