Lemma-based Dynamic Time Warping Search for Keyword Spotting Applications in Romanian

Keyword spotting is the task of identifying words of interest in audio streams. The task has been approached in the past using HMM-based speech decoders and more recently with deep neural networks. This paper presents a two-fold approach in which the audio stream is first transcribed into words and then the words of interest are searched for in the transcription. We focus on the search part which is specifically tailored for the Romanian language. We propose two search methods for identifying words of interest in the transcriptions, namely, direct string search and dynamic time warping-based search. Both of them are applied on raw and, for better performance, on lemmatized transcriptions. The methods are evaluated in terms of F-measure. We report F-measures of up to 92% for the search algorithm solely and up to 89% for the full system: speech transcription followed by keyword spotting.