In recent years, the features derived from posteriors of a multilayer perceptron (MLP), known as tandem features, have proven to be very effective for automatic speech recognition. Most tandem features to date have relied on MLPs trained for phone classification. We recently showed on a relatively small data set that MLPs trained for articulatory feature classification can be equally effective. In this paper, we provide a similar comparison using MLPs trained on a much larger data set - 2000 hours of English conversational telephone speech. We also explore how portable phone- and articulatory feature- based tandem features are in an entirely different language - Mandarin - without any retraining. We find that while phone-based features perform slightly better in the matched-language condition, they perform significantly better in the cross-language condition. Yet, in the cross-language condition, neither approach is as effective as the tandem features extracted from an MLP trained on a relatively small amount of in-domain data. Beyond feature concatenation, we also explore novel observation modelling schemes that allow for greater flexibility in combining the tandem and standard features at hidden Markov model (HMM) outputs.