  For a better understanding of the mechanisms underlying speech perception and
the contribution of different signal features, computational models of speech
recognition have a long tradition in hearing research. Due to the diverse range
of situations in which speech needs to be recognized, these models need to be
generalizable across many acoustic conditions, speakers, and languages. This
contribution examines the importance of different features for speech
recognition predictions of plain and Lombard speech for English in comparison
to Cantonese in stationary and modulated noise. While Cantonese is a tonal
language that encodes information in spectro-temporal features, the Lombard
effect is known to be associated with spectral changes in the speech signal.
These contrasting properties of tonal languages and the Lombard effect form an
interesting basis for the assessment of speech recognition models. Here, an
automatic speech recognition-based ASR model using spectral or spectro-temporal
features is evaluated with empirical data. The results indicate that
spectro-temporal features are crucial in order to predict the speaker-specific
speech recognition threshold SRT$_{50}$ in both Cantonese and English as well
as to account for the improvement of speech recognition in modulated noise,
while effects due to Lombard speech can already be predicted by spectral
features.
