Keyword spotting is a detection task consisting in discovering the presence of specific spoken words in unconstrained speech. The majority of keyword spotting systems are based on generative hidden Markov models and lack discriminative capabilities. However, discriminative keyword spotting systems are based on the estimation of a posteriori probabilities at the frame-level, hence they make use ...

Keyword spotting deals with the search of a reduced set of keywords in audio content. Phone Lattice-based approaches are very fast but achieve poor results. HMM-based keyword spotting systems deal with filler models to absorb the Out-of-vocabulary (OOV) words and achieve best results although they are slower. We propose a technique which combines them in order to perform a confidence measure to...

In this article, we propose an utterance verification technique for keyword spotting. The keyword spotting system analyzes a given spoken content and searches every speech segment in which one of pre-defined keywords is uttered. To maintain a stable recognition performance in the system, we propose an utterance verification technique that verifies whether a found utterance, or a candidate keywo...

Spoken keyword spotting (KWS) deals with the identification of keywords in audio streams and has become a fast-growing technology thanks to paradigm shift introduced by deep learning few years ago. This allowed rapid embedding KWS myriad small electronic devices different purposes like activation voice assistants. Prospects suggest sustained growth terms social use this technology. Thus, it is ...

We present the results of a small study involving the use of keyword-filler hidden Markov models (HMM) for spotting keywords in continuous speech. The performance dependence on the amount of keyword training data and the choice of model parameters is documented. Also, we demonstrate a strong correlation between individual keyword spotting performance and median duration of that keyword. This de...

Keyword spotting (or spoken term detection) is an interesting task in Music Information Retrieval that can be applied to a number of problems. Its purposes include topical search and improvements for genre classification. Keyword spotting is a well-researched task on pure speech, but state-of-the-art approaches cannot be easily transferred to singing because phoneme durations have much higher v...