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

The Performance Evaluation of Attention-Based Neural ASR under Mixed Speech Input