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3 months ago

The NNI Query-by-Example System for MediaEval 2014

Abstract

In this paper we describe the system proposed by NNI (NWPU-NTU-I2R) team for the QUESST task within the Mediaeval 2014 evaluation. To solve the problem, we used both dynamic time warping (DTW) and symbolic search (SS) based approaches. The DTW system performs template matching using subsequence DTW algorithm and posterior representations. The symbolic search is performed on phone sequences generated by phone recognizers. For both symbolic and DTW search, partial sequence matching is performed to reduce missing rate, especially for query type 2 and 3. After fusing 9 DTW systems, 7 symbolic systems, and query length side information, we obtained 0.6023 actual normalized cross entropy (actCnxe) for all queries combined. For type 3 complex queries, we achieved 0.7252 actCnxe.

Benchmarks

BenchmarkMethodologyMetrics
keyword-spotting-on-quesstNNI DTW(All Queries)
ATWV: 0.2918
Cnxe: 0.6925
MTWV: 0.2974
MinCnxe: 0.6816
keyword-spotting-on-quesstNNI non-filtered(for the development set)
ATWV: 0.0768
Cnxe: 6.0905
MTWV: 0.0767
MinCnxe: 0.9571
keyword-spotting-on-quesstNNI Choi(for the development set)
ATWV: 0.0692
Cnxe: 5.8940
MTWV: 0.0692
MinCnxe: 0.9595
keyword-spotting-on-quesstNNI Symbolic(All Queries)
ATWV: 0.2696
Cnxe: 0.7322
MTWV: 0.2717
MinCnxe: 0.7293

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