We introduce a multiengine speech processing system that can detect the location and the type of audio signal in variable noisy\r\nenvironments. This system detects the location of the audio source using a microphone array; the system examines the audio first,\r\ndetermines if it is speech/nonspeech, then estimates the value of the signal to noise (SNR) using a Discrete-Valued SNR Estimator.\r\nUsing this SNR value, instead of trying to adapt the speech signal to the speech processing system, we adapt the speech processing\r\nsystem to the surrounding environment of the captured speech signal. In this paper, we introduced the Discrete-Valued SNR\r\nEstimator and amultiengine classifier, usingMultiengine Selection orMultiengineWeighted Fusion. Also we use the SI as example\r\nof the speech processing. The Discrete-Valued SNR Estimator achieves an accuracy of 98.4% in characterizing the environment�s\r\nSNR. Compared to a conventional single engine SI system, the improvement in accuracy was as high as 9.0% and 10.0% for the\r\nMultiengine Selection and MultiengineWeighted Fusion, respectively.
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