Effective keyword search for low-resourced conversational speech

Lileikytė, Rasa; Fraga-Silva, Thiago; Lamel, Lori; Gauvain, Jean‐Luc; Laurent, Antoine; Huang, Guangpu

doi:10.1109/icassp.2017.7953265

Cited by 5 publications

(3 citation statements)

References 12 publications

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“…At present, the common methods for identifying out-of-vocabulary words are divided into the following two categories: rule-based approach and statistics-based approach [1][2] . Among them, the recognition of out-of-vocabulary words based on rules is based on the relevant principles of word formation and characteristics of speech to construct rules [3][4] .…”

Section: Related Workmentioning

confidence: 99%

Design and research of Chinese word segmentation method in architecture field

Fan¹,

Cao²,

Guan³

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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Section: Related Workmentioning

confidence: 99%

Design and research of Chinese word segmentation method in architecture field

Fan¹,

Cao²,

Guan³

2023

Third International Conference on Machine Learning and Computer Application (ICMLCA 2022)

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“…Subword-based ASR, on the other hand, can detect terms even though they are not in the vocabulary of the recognizer (i.e., out-of-vocabulary (OOV) terms). A more robust system could be obtained by combining both approaches [17,24,25,38,39,42,50,[67][68][69][70][71][72][73].…”

Section: 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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“…On the other hand, the subword-based approach has the unique advantage that it can detect terms that consist of words that are not in the vocabulary of the recognizer, i.e., out-ofvocabulary (OOV) terms. The combination of these two approaches has been proposed in order to exploit the relative advantages of word and subword-based strategies [17,32,33,36,44,[63][64][65][66][67][68][69][70].…”

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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Effective keyword search for low-resourced conversational speech

Cited by 5 publications

References 12 publications

Design and research of Chinese word segmentation method in architecture field

Design and research of Chinese word segmentation method in architecture field

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

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

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