Speech-music segmentation system for speech recognition

Using posterior probability based features to segment an audio signal as speech and music has been commonly used method. In this study Hidden-Markov-Model (HMM) based acoustic models are used to calculate posterior probabilities. Acoustic Models includes states of context-independent phones as modeling unit. Entropy and dynamism are found using via the posterior probabilities and these values are used as feature for speech-music discrimination. An HMM based classifier that uses Viterbi decoding is implemented and using discriminative features, audio signals are segmented as speech and music. As a result of the tests, it was found that applied speech-music segmentation method decreases Word-Error-Rate and increases the speed of recognition.