A novel approach for modeling non-keyword intervals in a keyword spotter exploiting acoustic similarities of languages
In this paper, we present a new keyword spotting technique. A critical issue in keyword spotting is the explicit modeling of the non-keyword portions. To date, most keyword spotters use a set of Hidden Markov Models (HMM) to represent the non-keyword portions. A widely used approach is to split the training data into keyword and non-keyword data. The keywords are represented by HMMs trained using the keyword speech, and the garbage models are trained using the non-keyword speech. The main disadvantage of this method is the task dependence. Another approach is to use a common set of acoustic models for both keywords and garbage models. However, this method faces a major problem. In a keyword spotter, the garbage models are usually connected to allow any sequence. Therefore, the keywords are also included in these sequences. When the same training data are used for keyword and garbage models, the garbage models also cover the keywords. In order to overcome these problems, we propose a new method for modeling the non-keyword intervals. In our method, the garbage models are phonemic HMMs trained using a speech corpus of a language other thanbut acoustically similar tothe target language. In our work, the target language is Japanese and, due to the high similarity, English was chosen as the garbage language for training the garbage models. Using English garbage modelsinstead of Japaneseour method achieves higher performance, compared with when Japanese garbage models are used. Moreover, parameter tuning (e.g., word insertion penalty) does not have a serious effect on the performance when English garbage models are used. Using clean telephone speech test data and a vocabulary of 100 keywords, we achieved a 7.9% equal error rate which is a very promising result. In this paper we also introduce results obtained using several sizes of vocabulary, and we investigate the selection of the most appropriate garbage model set. In addition to the Japanese keyword spotting system, we also introduce results of an English keyword spotter. By using Japanese garbage modelsinstead of Englishwe achieved significant improvement. Using telephone speech test data and a vocabulary of 25 keywords the achieved Figure of Merit (FOM) was 74.7% compared to 68.9% when English garbage models were used.
keyword spotting telephone speech two-pass garbage hidden markov models