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State-of-the-art automatic speech recognition (ASR) systems are usually based on hidden Markov models (HMMs) that emit cepstral-based features which are assumed to be piecewise stationary. While not really robust to noise, these features are also known to be very sensitive to "auxiliary" information, such as pitch, energy, rate-of-speech (ROS), etc. Attempts so far to include such auxiliary information in state-of-the-art ASR systems have often been based on simply appending these auxiliary features to the standard acoustic feature vectors. In the present paper, we investigate different approaches to incorporating this auxiliary information using dynamic Bayesian networks (DBNs) or hybrid HMM/ANNs (HMMs with artificial neural networks). These approaches are motivated by the fact that the auxiliary information is not necessarily (directly) emitted by the HMM states but, rather, carries higher-level information (e.g …