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Differential privacy (DP) is one data protection avenue to safeguard userinformation used for training deep models by imposing noisy distortion onprivacy data. Such a noise perturbation often results in a severe performancedegradation in automatic speech recognition (ASR) in order to meet a privacybudget $\varepsilon$. Private aggregation of teacher ensemble (PATE) utilizesensemble probabilities to improve ASR accuracy when dealing with the noiseeffects controlled by small values of $\varepsilon$. We extend PATE learning towork with dynamic patterns, namely speech utterances, and perform a firstexperimental demonstration that it prevents acoustic data leakage in ASRtraining. We evaluate three end-to-end deep models, including LAS, hybridCTC/attention, and RNN transducer, on the open-source LibriSpeech and TIMITcorpora. PATE learning-enhanced ASR models outperform the benchmark DP-SGDmechanisms, especially under strict DP budgets, giving relative word error ratereductions between 26.2% and 27.5% for an RNN transducer model evaluated withLibriSpeech. We also introduce a DP-preserving ASR solution for pretraining onpublic speech corpora.

An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition