1. A method for recognizing speech by determining the likelihood of observing a feature vector o t of a speech signal employing time and frequency Signal-to-Noise Ratio (SNR) dependent weighting, said method comprising the steps of: receiving a speech signal; for each time period t of the speech signal, estimating the SNR to get time and frequency SNR information η t,f ; calculating the time and frequency weighting to get weighting coefficient γ tf , wherein γ tf is a function of η t,f ; using an inverse Discrete Cosine Transform (DCT) matrix M −1 to transform a cepstral distance (o t -μ) associated with the speech time period t to a spectral distance; computing a weighted spectral distance by applying time and frequency weighting to the spectral distance employing a time-varying diagonal matrix G t which represents the weighting coefficient γ tf ; transforming the weighted spectral distance to a weighted cepstral distance employing a forward DCT matrix M; calculating a likelihood of observing the feature vector o t by employing the weighted cepstral distance in a probability function b j (o t ); and performing speech recognition of the speech signal employing the probability function b j (o t ) that is both time and frequency weighted.