  This paper investigates methods to effectively retrieve speaker information
from the personalized speaker adapted neural network acoustic models (AMs) in
automatic speech recognition (ASR). This problem is especially important in the
context of federated learning of ASR acoustic models where a global model is
learnt on the server based on the updates received from multiple clients. We
propose an approach to analyze information in neural network AMs based on a
neural network footprint on the so-called Indicator dataset. Using this method,
we develop two attack models that aim to infer speaker identity from the
updated personalized models without access to the actual users' speech data.
Experiments on the TED-LIUM 3 corpus demonstrate that the proposed approaches
are very effective and can provide equal error rate (EER) of 1-2%.
