Generative RNNs for OOV Keyword Search

Gundogdu, Batuhan; Yusuf, Bolaji; Saraçlar, Murat

doi:10.1109/lsp.2018.2881610

Cited by 13 publications

(9 citation statements)

References 17 publications

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“…[18] proposed an end‐to‐end keyword detection system without ASR; Batuhan et al. [19] proposed a query model based on RNN; Biswaranjan et al. [20] proposed a keyword recognition system based on Mel cepstral coefficients (MFCCs) extracted from smoothed spectrum.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…The data set we produced includes Chinese, Sichuanese (Chinese dialect), Cantonese (Chinese dialect), and English. The English data part consists of TIMIT corpus [19] and English TED speeches. The TIMIT data set contains a total of 6300 sentences, recorded by 630 people, each of whom utters a given ten sentences.…”

Section: Datasetmentioning

confidence: 99%

“…This experiment selected three network models as the experimental control group: Kartik. et al [18] proposed an end-to-end keyword detection system without ASR; Batuhan et al [19] proposed a query model based on RNN; Biswaranjan et al [20] proposed a keyword recognition system based on Mel In order to fully test the anti-interference ability of the network against noise, this paper supplements several sets of comparative experiments in noise environments, adding different intensities of Gaussian white noise, cafe noise, and street noise to the test speech. The experimental results are shown in Table 3.…”

Section: Performance Comparison Experimentsmentioning

confidence: 99%

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Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Yong-liang

2021

J. eng.

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Language keyword extraction has broad application prospects. Most current keyword search networks are based on speech recognition and text search. This method is helpless for some languages that are difficult to text and cannot locate the location where the keywords appear. A multi‐level structure of speech keyword location and recognition system (MSS‐Net), which can locate the location interval of keywords while recognizing keywords is proposed here. MSS‐Net contains three levels. The first level is the voice start and end detection module, which is responsible for detecting the start and end positions of valid audio. When valid audio is detected, the second level is activated: the keyword detection module. The keyword detection module detects keyword information in the audio signal and locates the keyword position interval. The third level is the keyword recognition module. The system recognizes the detected position interval of the location to obtain specific keyword information. Comparative experiments show that MSS‐Net is more accurate for keyword positioning, with a network recall rate of 94.91% and an accuracy rate of 96.32%, which is better than other network models participating in the experiment. In addition, the network has strong anti‐interference ability, which can achieve good suppression effect for normal life noise. The ablation experiment verified the effect of the hierarchical network structure on the performance of speech recognition tasks.

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Section: Experiments and Analysismentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Performance Comparison Experimentsmentioning

confidence: 99%

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Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Yong-liang

2021

J. eng.

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“…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%

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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“…Lexicon expansion methods [9,10] have also been applied, although the effectiveness of such methods naturally depends on how well they are able to preemptively cover the OOV terms without knowing them. DTW has also been used been used for vocabulary independent term retrieval in the context of both query-byexample (QBE) [11,12,13] and keyword search [14,15].…”

Section: Introductionmentioning

confidence: 99%

An Empirical Evaluation of DTW Subsampling Methods for Keyword Search

Yusuf

Saraçlar

2019

Interspeech 2019

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State of the art vocabulary-independent spoken term detection methods are typically based on variants of the dynamic time warping (DTW) algorithm since DTW, being based on acoustic sequence matching, allows robust retrieval in settings with scarcity of linguistic resources. However, the DTW comes with a high computational cost which limits its practicality in a deployed server. To this end, we investigate the efficacy of subsampling and propose a neural network architecture to reduce the computational load of DTW-based keyword search. We use a time-subsampled RNN to reduce the frame rate of the document as well as the dimensionality of representation while training it to maintain the cost incurred along the DTW alignment path, thus allowing us to reduce the computational complexity (both space and time) of the search algorithm. Experiments on the Turkish and Zulu limited language packs of the IARPA Babel program show that the proposed methods allow considerable reduction in CPU time (88 times) and memory usage (18 times) without significant loss in search accuracy (0.0270 ATWV). Moreover, even at very high compression levels with lower search precision, high recall rates are maintained, allowing the potential of multi-resolution search.

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Generative RNNs for OOV Keyword Search

Cited by 13 publications

References 17 publications

Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

Multilevel structured convolution neural network for speech keyword location and recognition: MSS‐Net

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

An Empirical Evaluation of DTW Subsampling Methods for Keyword Search

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