This paper investigates a new privacy-preserving paradigm for the task of query-by-example speech search using Secure Binary Embeddings, a hashing method that converts vector data to bit strings through a combination ...
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In this paper, we present the task and describe the main findings of the 2014 'query-by-example speech search Task' (QUESST) evaluation. The purpose of QUESST was to perform language independent search of spok...
详细信息
In this paper, we present the task and describe the main findings of the 2014 "query-by-example speech search Task" (QUESST) evaluation. The purpose of QUESST was to perform language independent search of sp...
详细信息
ISBN:
(纸本)9781467369985
In this paper, we present the task and describe the main findings of the 2014 "query-by-example speech search Task" (QUESST) evaluation. The purpose of QUESST was to perform language independent search of spoken queries on spoken documents, while targeting languages or acoustic conditions for which very few speech resources are available. This evaluation investigated for the first time the performance of query-by-examplesearch against morphological and morpho-syntactic variability, requiring participants to match variants of a spoken query in several languages of different morphological complexity. Another novelty is the use of the normalized cross entropy cost (Cnxe) as the primary performance metric, keeping Term-Weighted Value (TWV) as a secondary metric for comparison with previous evaluations. After analyzing the most competitive submissions (by five teams), we find that, although low-level "pattern matching" approaches provide the best performance for "exact" matches, "symbolic" approaches working on higher-level representations seem to perform better in more complex settings, such as matching morphological variants. Finally, optimizing the output scores for Cnxe seems to generate systems that are more robust to differences in the operating point and that also perform well in terms of TWV, whereas the opposite might not be always true.
This paper investigates a new privacy-preserving paradigm for the task of query-by-example speech search using Secure Binary Embeddings, a hashing method that converts vector data to bit strings through a combination ...
详细信息
ISBN:
(纸本)9781467369985
This paper investigates a new privacy-preserving paradigm for the task of query-by-example speech search using Secure Binary Embeddings, a hashing method that converts vector data to bit strings through a combination of random projections followed by banded quantization. The proposed method allows performing spoken querysearch in an encrypted domain, by analyzing ciphered information computed from the original recordings. Unlike other hashing techniques, the embeddings allow the computation of the distance between vectors that are close enough, but are not perfect matches. This paper shows how these hashes can be combined with Dynamic Time Warping based on posterior derived features to perform secure speechsearch. Experiments performed on a sub-set of the speech-Dat Portuguese corpus showed that the proposed privacy-preserving system obtains similar results to its non-private counterpart.
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