Improvements and applications for key word recognition using hidden Markov modeling techniques

Abstract

A basic assumption for most current speech recognition system is that the speech to be recognized consist solely of words from a predefined vocabulary. For speech recognition applications in the telephone network, it is naive to assume that users will adhere strictly to this protocol. In Wilpon. et al [1.2]. a hidden Markov model based key wordspotting algoridutl was presented, which can recognize key words from a pre-defined vocabulary list spoken in an unconstrained fashion. In OUT current work, we explore several improvements to the feature analysis used to represent the speech signal and modeling techniques used to train our system.Common measures for evaluating recognition systems are essential for comparing results and assessing claims. However, no such measure has yet been developed for key wordspotting systems. In our psper, we will discuss several task domain issues, which influence evaluation criteria.Scoring methodologies that fit our research goals will be described. We will present results from extensive evaluations on three speaker independent databases: the 20 word vocabulary Stonehenge Road Rally database, distributed by the National Security Agency (NSA), a five word vocabulary used to automate operatorassisted calls, and a three word Spanish vocabulary that is currently being trialed in Spain's telephone network. Currently, we are achieving recognition accuracies ranging from 99.9% on the Spanish database to 74% (with 8.8 FA/H/W) on the Stonehenge task.

Unsupervised spoken keyword spotting via segmental DTW on Gaussian posteriorgrams

“…Although large-vocabulary ASR methods have been shown to be very effective [6], a popular method incorporates parallel filler or background acoustic models to compete with keyword hypotheses [11], [12]. These keyword spotting methods typically require large amounts of transcribed data for training the acoustic model.…”

Small-footprint keyword spotting using deep neural networks

“…A commonly used technique for keyword spotting is the Keyword/Filler Hidden Markov Model (HMM) [5,6,7,8,9]. Despite being initially proposed over two decades ago, it remains highly competitive.…”

Model Shrinking for Embedded Keyword Spotting

“…Another widely used approach for keyword spotting builds hidden Markov models (HMM) for each keyword and nonkeyword audio signal [5], [6], [7], [8]. The non-keyword audio signal includes other spoken words, background noise, etc.…”