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As automatic speech recognition-based applications become increasingly common in a wide variety of market segments, there is a growing need to support more languages. However, for many languages, the language resources needed to train speech recognition engines are either limited or completely non-existent, and the process of acquiring or constructing new language resources is both long and costly. This paper suggests a methodology that enables Phonetic Search Keyword Spotting to be implemented in a large speech database of any given under-resourced language using cross-language phoneme mappings to another language. The phoneme mapping enables a speech recognition engine from a sufficiently resourced and well-trained source language to be used for phoneme recognition in the new target language. The keyword search is then performed over a lattice of target language phonemes. Three cross-language phoneme mapping techniques are examined: knowledge-based, data-driven and phoneme recognition performance-based. The results suggest that Phonetic Search Keyword Spotting based on the cross-language phoneme mapping approach proposed herein can serve as a quick initial solution for validating keyword spotting applications in new, under-resourced languages.

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