A survey on structured discriminative spoken keyword spotting

被引：3

作者：

Tabibian, Shima ^{[1
]}

机构：

[1] Shahid Beheshti Univ, Cyberspace Res Inst, Shahid Shahriari Sq,Daneshjou Blvd, Tehran 1983969411, Iran

来源：

ARTIFICIAL INTELLIGENCE REVIEW | 2020年 / 53卷 / 04期

关键词：

Deep learning; Discriminative model; Hidden Markov model; Spoken keyword spotting; Structured data; HIDDEN MARKOV-MODELS; SPEECH RECOGNITION; CONFIDENCE MEASURES; ALGORITHM; NORMALIZATION; COMPRESSION; CLASSIFIER; NETWORKS; PHONEME; SEARCH;

D O I：

10.1007/s10462-019-09739-y

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Spoken keyword spotting refers to the detection of all occurrences of desired words in continuous speech utterances. This paper includes a comprehensive review on various spoken keyword spotting (especially discriminative spoken keyword spotting) approaches. The most common datasets and evaluation measures for training and evaluating the spoken keyword spotting systems are reviewed in this paper. Moreover, the main framework for structured discriminative spoken keyword spotting (SDKWS) is presented. Different parts of the SDKWS framework such as feature extraction, model training, search algorithm and thresholding are discussed in this paper. Finally, the paper is concluded in the conclusion section and the future works are presented in the last part of that section.

引用

页码：2483 / 2520

页数：38

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