In this paper, we address the problem of noise compensation in speech signals for robust speech recognition. Several classical denoising methods in the field of speech and signal processing are compared on speech corrupted by music, as it is often the case in broadcast news transcription tasks. We also present two new source separation techniques, namely adaptiveWiener filtering and adaptive shrinkage. These techniques rely on the use of a dictionary of spectral shapes in order to tackle the problem of non stationarity of the signals. The algorithms are first compared on the source separation task and assessed in terms of average distortion. Their effect on the entire transcription system is eventually compared in terms of word error rate. Results show that the proposed adaptive Wiener filter approach yields a significant improvement of the transcription accuracy at signal/noise ratios greater than 15 dB.