This paper aims at assessing the automatic labeling of an undocumented, unknown, unwritten and under-resourced language (Mo Piu) of the North Vietnam, by an expert phonetician. In the previous stage of the work, 7 sets of languages were chosen among Mandarin, Vietnamese, Khmer, English, French, to compete in order to select the best models of languages to be used for the phonetic labeling of Mo Piu isolated words. Two sets of languages (1° Mandarin + French, 2° Vietnamese + French) which got the best scores showed an additional distribution of their results. Our aim is now to study this distribution more precisely and more extensively, in order to statistically select the best models of languages and among them, the best sets of phonetic units which minimize the wrong phonetic automatic labeling.

In this paper we present an overview on the development of a large vocabulary continuous speech recognition (LVCSR) system for Khmer, the official language of Cambodia, spoken by more than 15 million people. As an under-resourced language, develop a LVCSR system for Khmer is a challenging task. We describe our methodologies for quick language data collection and processing for language modeling and acoustic modeling. For language modeling, we investigate the use of word and sub-word as basic modeling unit in order to see the potential of sub-word units in the case of unsegmented language like Khmer. Grapheme-based acoustic modeling is used to quickly build our Khmer language acoustic model. Furthermore, the approaches and tools used for the development of our system are documented and made publicly available on the web. We hope this will contribute to accelerate the development of LVCSR system for a new language, especially for under-resource languages of developing countries where resources and expertise are limited.