Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Incremental Transfer Learning in Two-pass Information Bottleneck based
  Speaker Diarization System for Meetings   The two-pass information bottleneck (TPIB) based speaker diarization system
operates independently on different conversational recordings. TPIB system does
not consider previously learned speaker discriminative information while
diarizing new conversations. Hence, the real time factor (RTF) of TPIB system
is high owing to the training time required for the artificial neural network
(ANN). This paper attempts to improve the RTF of the TPIB system using an
incremental transfer learning approach where the parameters learned by the ANN
from other conversations are updated using current conversation rather than
learning parameters from scratch. This reduces the RTF significantly. The
effectiveness of the proposed approach compared to the baseline IB and the TPIB
systems is demonstrated on standard NIST and AMI conversational meeting
datasets. With a minor degradation in performance, the proposed system shows a
significant improvement of 33.07% and 24.45% in RTF with respect to TPIB system
on the NIST RT-04Eval and AMI-1 datasets, respectively.

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The categories of this paper are: atom-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: adap-org
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The categories of this paper are: hep-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: physics
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The categories of this paper are: hep-ex
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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: q-fin