A hybrid neural network, dynamic programming word spotter

Abstract

A novel keyword-spotting system that combines both neural network and dynamic programming techniques is presented. This system makes use of the strengths of time delay neural networks (TDNNs), which include strong generalization ability, potential for parallel implementations, robustness to noise, and time shift invariant learning. Dynamic programming models are used by this system because they have the useful capability of time warping input speech patterns. This system was trained and tested on the Stonehenge Road Rally database, which is a 20-keyword-vocabulary, speaker-independent, continuous-speech corpus. Currently, this system performs at a figure of merit (FOM) rate of 82.5%. FOM is the detection rate averaged from 0 to 10 false alarms per keyword hour. This measure is explained in detail.<>