Abstract. Application domains such as in-car human-machine interaction require noise-robust front-ends in order to cope with the noisy situations encountered in practice. Moreover, when speech is captured through a cellphone, the phone channel characteristics are often unknown. It is thus desirable to estimate and remove both phone channel characteristics and ambient noise, in an online manner. The main contributions of this paper are twofold. First, a novel channel normalization method is proposed, that is used before noise reduction, at the magnitude spectrogram level. It removes the convolutive channel, and reduces the stationary part of the ambient noise. Second, an alternative to classical spectral subtraction is proposed, called “Unsupervised Spectral Subtraction ” (USS), which does not require any parameter tuning. Channel normalization followed by USS (two steps) permit to reach an ASR performance very similar to that of the ETSI Advanced Front-End (Wiener filtering, with many steps and parameters). The computational cost of the proposed approach is very low, which makes it fit for real-time applications. Furthermore, channel normalization followed by the ETSI Advanced Front-End leads to a major improvement in noisy conditions, and best overall results. 2 IDIAP–RR 06-09 s(t) h(t) x(t) n(t) Figure 1: Model of the problem: recognize speech from the observed signal x(t) = h(t) ∗ (s(t) + n(t)), where n(t) is the additive acoustic noise and h(t) is the transmission channel (e.g. cellphone).