This paper discusses about effective speech characterization for off-line voice activity detection (VAD), which is an important step prior to speech data mining. Five different natures of speech are examined; energy, spectral shape, periodicity, phonetic variation, and spectral fluctuation, the latter observed from a new point of view. Specific spectral fluctuation patterns of speech have been analyzed using multi-stage Harmonic/Percussive Sound Separation algorithm. We compared the performance of the features, and various combinations, to evaluate their robustness in multiple noise environments. The combined approach outperformed the baseline of CENSREC-1-C evaluation framework. The results suggest that the proposed feature extraction approach can improve state of the art VAD methods.
['Miquel Espi', 'Shigeki Miyabe', 'Takuya Nishimoto', 'Nobutaka Ono', 'Shigeki Sagayama']
Analysis on speech characteristics for robust voice activity detection