Unlike English, many languages use phonetic writing style, e.g., the Slavic languages, Turkish, Hungarian, etc. For these languages the pronunciation modeling for an ASR (Automatic Speech Recognition) system is relatively easy, because merely some transcription rules have to be developed instead of huge dictionaries. Due to rule-based automatic transcriptions, such a solution can be much more flexible than dictionary-based ones since dynamically changing vocabularies can be transcribed without a-priori knowledge of the input words. This chapter discusses rule-based automatic phonetic transcriptions developed for Hungarian speech recognition. It first introduces the basic technologies of automatic speech recognition for the sake of readers not familiar with this scientific field; then it discusses the role of phonetic transcription in speech recogniser training. Next, our method is presented for transcribing Hungarian texts automatically. This technique is an extension of the traditional linear transcription approach; its output is called ‘optioned’ because it contains pronunciation options - including cross-word coarticulations - in parallel arcs. Comparing our ‘optioned’ transcription to other kinds of transcriptions, significant improvements in recogniser training efficiency can be experienced. The acoustic models trained with our automatically made phonetic transcriptions perform on independent test data practically at the same level as the acoustic models obtained using manual phonetic segmentation of the whole training database.