This paper proposes an efficient anti-keyword derivation method to improve the rejection performance of keyword spotting. In this method, each anti-keyword is derived from the large vocabulary lexicon considering acoustic similarity to keywords, making use of the confusion matrix. Experimental results show that a 3 % improvement of the rejection rate is obtained compared to conventional methods...

We present a novel architecture for word-spotting which is trained from a small number of examples to classify an utterance as containing a target keyword or not. The word-spotting architecture relies on a novel feature set consisting of a set of ordered spectro-temporal patches which are extracted from the exemplar mel-spectra of target keywords. A local pooling operation across frequency and ...

Keyword spotting is a very forward-looking and promising branch of speech recognition. This paper presents a HMM-based keyword spotting system, which works with a new algorithm. The first discussion topic is the description of the search algorithm, that needs no representation of the non-keyword parts of the speech signal. For this purpose, the computation of the HMM scores and the Viterbi algo...

This paper introduces the methods to verify keyword hypothesis using alignment information, which can be easily implemented and integrated into the keyword spotting system in a straightforward way. Acquiring alignment information goes without any extra model training, and has no evident total processing time impact on original keyword spotting system. Different alignment-based confidence measur...

In agglutinative languages like Korean, words are formed by joining l affix morphemes to the stem, which leads to high OOV rate in dictionary building. Hence, subword units are usually used as basic language modeling units in Large-Vocabulary Continuous Speech Recognition (LVCSR) or LVCSR based applications such as keyword spotting. In this work, firstly a new word property called coalescence t...

For languages with limited training resources, out-ofvocabulary (OOV) words are a significant problem, both for transcription and keyword spotting. This paper investigates the use of subword lexical units for keyword spotting. Three strategies for using the sub-word units are explored: 1) converting word-based lattices to subword lattices after decoding, 2) performing a separate decoding for ea...

Keyword Spotting (KWS) is an essential component in a smart device for alerting the system when user prompts it with command. As these devices are typically constrained by computational and energy resources, KWS model should be designed small footprint. In our previous work, we developed lightweight dynamic filters which extract robust feature map within noisy environment. The learning variable...