Abstract
The speech signal is the result of many nonlinearly interacting processes; therefore any linear analysis has the potential risk of missing a great amount of information content. Recently the technique of Empirical Mode Decomposition (EMD) has been proposed as a new tool for analysis of non linear and non stationary data. This paper deals with this new tool, to detect usable speech in co-channel speech. We applied EMD analysis to decompose co-channel speech signal into intrinsic oscillatory modes. Detected usable speech segments are organized into speaker streams, which are applied to speaker identification system (SID). The system is evaluated on co-channel speech across various Targets to Interferer Ratios (TIR). Performance evaluation has shown that EMD performs better than the linear dyadic wavelet decomposition based methods for usable speech detection.
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Ghezaiel, W., Ben Slimane, A., Ben Braiek, E. (2013). Improved EMD Usable Speech Detection for Co-channel Speaker Identification. In: Drugman, T., Dutoit, T. (eds) Advances in Nonlinear Speech Processing. NOLISP 2013. Lecture Notes in Computer Science(), vol 7911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38847-7_24
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DOI: https://doi.org/10.1007/978-3-642-38847-7_24
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