Wavelet method for breath detection in audio signals

An algorithm for automatic detection of breath events in a speech signal is suggested in this paper. The issues of breath events occurrences in recordings are discussed as well as their statistical parameters. Also the role of breath pauses for signalizing punctuation and emotional or physical state of the speaker, in both spontaneous and read speech, is described. Wavelet parameters of energy in frequency subbands are obtained from discrete packet wavelet decomposition with mel-scale as patterns of breaths. In the beginning of the detection procedure, preliminary hypotheses of breath are indicated in the analyzed speech signal using temporal features. Then, discrete wavelet transform parameters are calculated. Dynamic time warping is applied to establish final recognition. The algorithm will be used for automatic speech recognition for measuring similarity between training and test features vectors.