Constructing sub-word units for spoken term detection

Heerden, Charl van; Karakos, Damianos; Narasimhan, Karthik; Davel, Marelie H.; Schwartz, Richard

doi:10.1109/icassp.2017.7953264

Cited by 16 publications

(10 citation statements)

References 17 publications

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“…However, subword-based ASR [22,24,[60][61][62][63][64][65][66] is also being used, sometimes in combination with word-based ASR. One of the main challenges of using word-based ASR in this context is that, in principle, only in-vocabulary (INV) terms can be detected.…”

Section: Spoken Term Detectionmentioning

confidence: 99%

“…This program focused on building fully automatic and noise-robust speech recognition and search systems in a very limited amount of time (e.g., one week) and with limited amount of training data. The languages addressed in that program were low-resourced, such as Cantonese, Pashto, Tagalog, Turkish, Vietnamese, Swahili, Tamil and so on, and significant research has been carried out [13,61,[147][148][149][150][151][152][153][154][155][156][157][158][159].…”

Section: Comparison With Previous Std International Evaluationsmentioning

confidence: 99%

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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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Section: Spoken Term Detectionmentioning

confidence: 99%

Section: Comparison With Previous Std International Evaluationsmentioning

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 novel problem of lexeme-set KWS is related to work on out-of-vocabulary KWS, which has been approached by handling sub-word units such as syllables and morphemes (Trmal et al, 2014;Narasimhan et al, 2014;van Heerden et al, 2017;He et al, 2016). In contrast to KWS with sub-word granularity, our approach is to generate likely full-word inflections given a lemma.…”

Section: Abstractearchmentioning

confidence: 99%

Induced Inflection-Set Keyword Search in Speech

Adams¹,

Wiesner²,

Trmal³

et al. 2020

Proceedings of the 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology

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We investigate the problem of searching for a lexeme-set in speech by searching for its inflectional variants. Experimental results indicate how lexeme-set search performance changes with the number of hypothesized inflections, while ablation experiments highlight the relative importance of different components in the lexeme-set search pipeline and the value of using curated inflectional paradigms. We provide a recipe and evaluation set for the community to use as an extrinsic measure of the performance of inflection generation approaches.

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“…For the ASR stage, word-based speech recognition has been widely used [35,[48][49][50][51][52][53][54], since this typically yields better performance than subword-based ASR [55][56][57][58][59][60][61][62] due to the lexical and language model (LM) information employed by the word-based ASR. However, one of the main drawbacks of word-based ASR is that it can only detect in-vocabulary (INV) terms.…”

Section: Spoken Term Detection Overviewmentioning

confidence: 99%

ALBAYZIN 2018 spoken term detection evaluation: a multi-domain international evaluation in Spanish

Tejedor

Toledano

López-Otero

et al. 2019

J AUDIO SPEECH MUSIC PROC.

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Search on speech (SoS) is a challenging area due to the huge amount of information stored in audio and video repositories. Spoken term detection (STD) is an SoS-related task aiming to retrieve data from a speech repository given a textual representation of a search term (which can include one or more words). This paper presents a multi-domain internationally open evaluation for STD in Spanish. The evaluation has been designed carefully so that several analyses of the main results can be carried out. The evaluation task aims at retrieving the speech files that contain the terms, 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: the MAVIR database, which comprises a set of talks from workshops; the RTVE database, which includes broadcast news programs; and the COREMAH database, which contains 2-people spontaneous speech conversations about different topics. We present the evaluation itself, the three databases, the evaluation metric, the systems submitted to the evaluation, the results, and detailed post-evaluation analyses based on some term properties (within-vocabulary/out-of-vocabulary terms, single-word/multi-word terms, and native/foreign terms). Fusion results of the primary systems submitted to the evaluation are also presented. Three different research groups took part in the evaluation, and 11 different systems were submitted. The obtained results suggest that the STD task is still in progress and performance is highly sensitive to changes in the data domain.

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Constructing sub-word units for spoken term detection

Cited by 16 publications

References 17 publications

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

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

Induced Inflection-Set Keyword Search in Speech

ALBAYZIN 2018 spoken term detection evaluation: a multi-domain international evaluation in Spanish

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