Syllables play an important role in speech synthesis and recognition. Prosodic information is embedded into syllable units of speech. Here we present a method for semi-automatic syllable labelling of Assamese speech utterances using Hidden Markov Models (HMMs) and vowel onset-offset points. Semi-automatic syl- lable labelling means syllable labelling of the speech signal when transcription or the text corresponding to the speech file is provided. HMM models for 15 broad classes of phone is built. Time label of the transcription is obtained by the forced alignment procedure using the HMM models. A parser is used to convert the word transcription to syllable transcription using certain syllabification rules. This syllable transcription and the time label of the phones are used to get the time label of the syllables. Now the syllable labelling output is refined using the knowledge of vowel onset point and vowel offset point derived from the speech signal using different signal processing techniques. This refinement gives improvement in terms of both syllable detection as well as average deviation in the syllable onset and offset. © Springer India 2015.