This paper describes a language independent method for automatic syllabification of speech signal. This method utilizes the valleys in short time energy (STE) contour and location of vowel onset points (VOP) for marking the syllable boundaries. In the proposed method, automatic syllabification is performed in three steps. First, long silence/pause regions are marked with the help of speech/non-speech detection. Then VOPs are located from the Hilbert Envelope of LP residual. The existence of more than one VOP in a continuous speech region (identified using speech/non-speech detection in the first step) is an indication of syllable boundaries within the region. Location with minimum energy in the STE contour between two consecutive VOP is identified as the syllable boundary. Since automatic VOP detection algorithm fails to detect some of the VOPs, certain syllable boundaries will be missed. Therefore, at the third step, additional syllable boundaries are detected from STE contour by fixing a valley threshold which is equal to the mean value of STE corresponding to each speech region between two consecutive syllable boundaries. This method is evaluated for 50 sentences each in read, extempore and conversational mode speech of Malayalam and Bengali languages. Overall accuracy of 80% is obtained with ± 50 ms tolerance with reference to manually marked syllable boundaries for this database. Method also shows good accuracy in case of TIMIT and NTIMIT data without tuning of thresholds and other parameters. This method is useful for applications that do not require exact syllable boundaries, rather a meaningful separation of syllables. Application of this technique for prosody based emotion recognition is illustrated using Emo-DB German emotional database. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.