Search on speech from spoken queries: the Multi-domain International ALBAYZIN 2018 Query-by-Example Spoken Term Detection Evaluation

Abstract

The huge amount of information stored in audio and video repositories makes search on speech (SoS) a priority area nowadays. Within SoS, Query-by-Example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given a spoken query. Research on this area is continuously fostered with the organization of QbE STD evaluations. This paper presents a multi-domain internationally open evaluation for QbE STD in Spanish. The evaluation aims at retrieving the speech files that contain the queries, providing their start and end times, and a score that reflects the confidence given to the detection. Three different Spanish speech databases that encompass different domains have been employed in the evaluation: MAVIR database, which comprises a set of talks from workshops; RTVE database, which includes broadcast television (TV) shows; and COREMAH database, which contains 2-people spontaneous speech conversations about different topics. The evaluation has been designed carefully so that several analyses of the main results can be carried out. We present the evaluation itself, the three databases, the evaluation metrics, the systems submitted to the evaluation, the results, and the detailed post-evaluation analyses based on some query properties (within-vocabulary/out-of-vocabulary queries, single-word/multi-word queries, and native/foreign queries). Fusion results of the primary systems submitted to the evaluation are also presented. Three different teams took part in the evaluation, and ten different systems were submitted. The results suggest that the QbE STD task is still in progress, and the performance of these systems is highly sensitive to changes in the data domain. Nevertheless, QbE STD strategies are able to outperform text-based STD in unseen data domains.

Contract Discovery: Dataset and a Few-Shot Semantic Retrieval Challenge with Competitive Baselines

“…These include THUMOS 14 (Jiang et al, 2014) as well as ActivityNet 1.2 and ActivityNet 1.3 challenges (Fabian Caba Heilbron and Niebles, 2015). Another example is queryby-example spoken term detection, as considered e.g., in ALBAYZIN 2018 challenge (Tejedor et al, 2019).…”

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“…However, this approach cannot meet the requirements of speed and quality at the same time in practical applications. Thus, to avoid the decoding process of ASR, some methods [4][5][6] directly use the acoustic modeling part of ASR model to extract the features of audio signals, and then compare these features of different lengths by dynamic time wrapping (DTW) [7].…”