We describe two voice-to-phoneme conversion algorithms for speaker-independent voice-tag creation specifically targeted at applications on embedded platforms. These algorithms (batch mode and sequential) are compared in speech recognition experiments where they are first applied in a same-language context in which both acoustic model training and voice-tag creation and application are performed on the same language. Then, their performance is tested in a cross-language setting where the acoustic models are trained on a particular source language while the voice-tags are created and applied on a different target language. In the same-language environment, both algorithms either perform comparably to or significantly better than the baseline where utterances are manually transcribed by a phonetician. In the cross-language context, the voice-tag performances vary depending on the source-target language pair, with the variation reflecting predicted phonological similarity between the source and target languages. Among the most similar languages, performance nears that of the native-trained models and surpasses the native reference baseline.