In this paper we describe a generic architecture for single channel speech enhancement. We assume processing in frequency domain and suppression based speech enhancement methods. The framework consists of a two stage voice activity detector, noise variance estimator, a suppression rule, and an uncertain presence of the speech signal modifier. The evaluation corpus is a synthetic mixture of a clean speech (TIMIT database) and in-car recorded noises. Using the framework multiple speech enhancement algorithms are tuned for maximum performance. We propose a formalized procedure for automated tuning of these algorithms. The optimization criterion is a weighted sum of the mean opinion score (PESQ-MOS), signalto-noise-ratio (SNR), log-spectral distance (LSD), and mean square error (MSE). The proposed framework provides a complete speech enhancement chain and can be used for evaluation and tuning of other suppression rules and voice activity detector algorithms.