Tackling unseen acoustic conditions in query-by-example search using time and frequency convolution for multilingual deep bottleneck features

Hout, Julien van; Mitra, Vikramjit; Franco, Horacio; Bartels, Chris; Vergyri, Dimitra

doi:10.1109/asru.2017.8268915

Cited by 3 publications

(2 citation statements)

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“…Regarding the features used for query/utterance representation, Gaussian posteriorgrams are employed in [22,29,40,41]; an i-vector-based approach for feature extraction is proposed in [42]; phone log-likelihood ratio-based features are used in [43]; posteriorgrams derived from various unsupervised tokenizers, supervised tokenizers, and semi-supervised tokenizers are employed in [44]; and posteriorgrams derived from a Gaussian mixture model (GMM) tokenizer, phoneme recognition, and acoustic segment modeling are used in [45]. Phoneme posteriorgrams have been widely used [34,41,[46][47][48][49][50][51][52][53][54] and bottleneck features as well [34,[55][56][57][58][59][60].…”

Section: Methods Based On Template Matchingmentioning

confidence: 99%

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

Tejedor

Toledano

López-Otero

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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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.

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Section: Methods Based On Template Matchingmentioning

confidence: 99%

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

Tejedor

Toledano

López-Otero

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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show abstract

“…Finally, there are also works exploring other types of posteriorgrams [107][108][109][110]. In recent years, the use of bottleneck features extracted from DNNs became popular [87,92,93,[111][112][113][114][115].…”

Section: Query-by-example Spoken Term Detectionmentioning

confidence: 99%

The Multi-Domain International Search on Speech 2020 ALBAYZIN Evaluation: Overview, Systems, Results, Discussion and Post-Evaluation Analyses

et al. 2021

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The large amount of information stored in audio and video repositories makes search on speech (SoS) a challenging area that is continuously receiving much interest. Within SoS, spoken term detection (STD) aims to retrieve speech data given a text-based representation of a search query (which can include one or more words). On the other hand, query-by-example spoken term detection (QbE STD) aims to retrieve speech data given an acoustic representation of a search query. This is the first paper that presents an internationally open multi-domain evaluation for SoS in Spanish that includes both STD and QbE STD tasks. The evaluation was carefully designed so that several post-evaluation analyses of the main results could be carried out. The evaluation tasks aim to retrieve the speech files that contain the queries, providing their start and end times and a score that reflects how likely the detection within the given time intervals and speech file is. Three different speech databases in Spanish that comprise different domains were employed in the evaluation: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the SPARL20 database, which contains Spanish parliament sessions. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the evaluation results and some detailed post-evaluation analyses based on specific query properties (in-vocabulary/out-of-vocabulary queries, single-word/multi-word queries and native/foreign queries). The most novel features of the submitted systems are a data augmentation technique for the STD task and an end-to-end system for the QbE STD task. The obtained results suggest that there is clearly room for improvement in the SoS task and that performance is highly sensitive to changes in the data domain.

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Multilingual spoken term detection: a review

Deekshitha

Mary²

2020

Int J Speech Technol

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Tackling unseen acoustic conditions in query-by-example search using time and frequency convolution for multilingual deep bottleneck features

Cited by 3 publications

References 12 publications

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

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

The Multi-Domain International Search on Speech 2020 ALBAYZIN Evaluation: Overview, Systems, Results, Discussion and Post-Evaluation Analyses

Multilingual spoken term detection: a review

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