Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Privacy attacks for automatic speech recognition acoustic models in a
  federated learning framework   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%.

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The categories of this paper are: physics
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: q-fin
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The categories of this paper are: math
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
16
The categories of this paper are: stat
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
24
The categories of this paper are: dg-ga