We present a novel method of integrating the likelihoods of multiple feature streams for robust speech recognition. The integration algorithm dynamically calculates a frame-wise stream weight so that a heavier weight is given to a stream that is robust to a variety of noisy environments or speaking styles. Such a robust stream is expected to bring out discriminative ability. The weight is calculated in real time from mutual information between an input stream and active HMM states in a search space. In this paper, we describe three features that are extracted through auditory filters by taking into account the human auditory system extracting amplitude and frequency modulations. These features are expected to provide complementary clues for speech recognition. Speech recognition experiments using field reports and spontaneous commentary from Japanese broadcast news showed that the proposed method reduced error words by 9% relative to the best result obtained from a single stream.