  In order to evaluate the performance of the attention based neural ASR under
noisy conditions, the current trend is to present hours of various noisy speech
data to the model and measure the overall word/phoneme error rate (W/PER). In
general, it is unclear how these models perform when exposed to a cocktail
party setup in which two or more speakers are active. In this paper, we present
the mixtures of speech signals to a popular attention-based neural ASR, known
as Listen, Attend, and Spell (LAS), at different target-to-interference ratio
(TIR) and measure the phoneme error rate. In particular, we investigate in
details when two phonemes are mixed what will be the predicted phoneme; in this
fashion we build a model in which the most probable predictions for a phoneme
are given. We found a 65% relative increase in PER when LAS was presented with
mixed speech signals at TIR = 0 dB and the performance approaches the unmixed
scenario at TIR = 30 dB. Our results show the model, when presented with mixed
phonemes signals, tend to predict those that have higher accuracies during
evaluation of original phoneme signals.
