Query by example search with segmented dynamic time warping for non-exact spoken queries

Proenga, Jorge; Veiga, Arlindo; Perdigão, Fernando

doi:10.1109/eusipco.2015.7362666

Cited by 11 publications

(8 citation statements)

References 10 publications

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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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“…Regarding the features used for query/utterance representation, [5,[13][14][15] employ Gaussian posteriorgrams; [16] proposes an i-vector-based approach for feature extraction; [17] uses phone log-likelihood ratio-based features; [18] employs posteriorgrams derived from various unsupervised tokenizers, supervised tokenizers, and semi-supervised tokenizers; [19] employs posteriorgrams derived from a Gaussian mixture model (GMM) tokenizer, phoneme recognition, and acoustic segment modelling; [11,15,[20][21][22][23][24][25][26] use phoneme posteriorgrams; [11,[27][28][29] employ bottleneck features; [30] employs posteriorgrams from non-parametric Bayesian models; [31] employs articulatory class-based posteriorgrams; [32] proposes an intrinsic spectral analysis; and [33] is based on the unsupervised segment-based bag of an acoustic words framework. All these studies employ the standard DTW algorithm for query search, except for [13], which employs the NS-DTW algorithm, [15,24,25,28,30], which employ the subsequence DTW (S-DTW) algorithm, [14], which presents a variant of the S-DTW algorithm, and [26], which employs the segmental DTW algorithm.…”

Section: Methods Based On Template Matching Of Featuresmentioning

confidence: 99%

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

Tejedor

Toledano

López-Otero

et al. 2018

J AUDIO SPEECH MUSIC PROC.

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Query-by-example Spoken Term Detection (QbE STD) aims to retrieve data from a speech repository given an acoustic (spoken) query containing the term of interest as the input. This paper presents the systems submitted to the ALBAYZIN QbE STD 2016 Evaluation held as a part of the ALBAYZIN 2016 Evaluation Campaign at the IberSPEECH 2016 conference. Special attention was given to the evaluation design so that a thorough post-analysis of the main results could be carried out. Two different Spanish speech databases, which cover different acoustic and language domains, were used in the evaluation: the MAVIR database, which consists of a set of talks from workshops, and the EPIC database, which consists of a set of European Parliament sessions in Spanish. We present the evaluation design, both databases, the evaluation metric, the systems submitted to the evaluation, the results, and a thorough analysis and discussion. Four different research groups participated in the evaluation, and a total of eight template matching-based systems were submitted. We compare the systems submitted to the evaluation and make an in-depth analysis based on some properties of the spoken queries, such as query length, single-word/multi-word queries, and in-language/out-of-language queries.

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“…In the 2014 Queryby-Example Speech Search Task (QUESST) [11], one task was non-exact matching, in which test occurrences could contain small morphological variations with regard to the lexical form of the query. To solve this problem, Xu et al [12] proposed a partial matching strategy in which all partial phone sequences of a query were used to search for matching instances; Proenga et al [13] some of these works attempted to solve the non-exact matching problem, they all used DTW-based matching on frame-level representations, which has been shown to be outperformed by distance-based matching on acoustic word embeddings [2,3,4].…”

Section: Related Workmentioning

confidence: 99%

Linguistically-Informed Training of Acoustic Word Embeddings for Low-Resource Languages

Yang

Hirschberg

2019

Interspeech 2019

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Acoustic word embeddings have been proven to be useful in query-by-example keyword search. Such embeddings are typically trained to distinguish the same word from a different word using exact orthographic representations; so, two different words will have dissimilar embeddings even if they are pronounced similarly or share the same stem. However, in real-world applications such as keyword search in low-resource languages, models are expected to find all derived and inflected forms for a certain keyword. In this paper, we address this mismatch by incorporating linguistic information when training neural acoustic word embeddings. We propose two linguistically-informed methods for training these embeddings, both of which, when we use metrics that consider non-exact matches, outperform state-of-the-art models on the Switchboard dataset. We also present results on Sinhala to show that models trained on English can be directly transferred to embed spoken words in a very different language with high accuracy.

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Query by example search with segmented dynamic time warping for non-exact spoken queries

Cited by 11 publications

References 10 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

ALBAYZIN Query-by-example Spoken Term Detection 2016 evaluation

Linguistically-Informed Training of Acoustic Word Embeddings for Low-Resource Languages

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