Robust Speech Detection Based on Phoneme Recognition Features
We introduce new method for discriminating speech and non-speech segments in audio signals based on the transcriptions produced by phoneme recognizers. Four measures based on consonant-vowels and voiced-unvoiced pairs obtained from different phonemes speech recognizers were proposed. They were constructed in a way to be recognizer and language independent and could be applied in different segmentation-classification frameworks. The segmentation systems were evaluated on different broadcast news datasets consisted of more than 60 hours of multilingual BN shows. The results of these evaluations illustrate the robustness of the proposed features in comparison to MFCC and posterior probability based features. The overall frame accuracies of the proposed approaches varied in range from 95% to 98% and remained stable through different test conditions and different phoneme recognizers.
KeywordsAudio Signal Automatic Speech Recognition Probability Weight Broadcast News Evaluation Dataset
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