Re-ranking of spoken term detections using CRF-based triphone detection models

Sawada, Naoki; Natori, Satoshi; Nishizaki, Hiromitsu

doi:10.1109/apsipa.2014.7041550

Cited by 2 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, deep learning, multiple linear regression, support vector machines (SVMs), and multilayer perceptrons have been used to estimate the confidence level of detected candidates in decision [13]- [15] and re-ranking processes [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords

Domoto

Utsuro

Sawada

et al. 2016

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARYThis study presents a two-stage spoken term detection (STD) method that uses the same STD engine twice and a support vector machine (SVM)-based classifier to verify detected terms from the STD engine's output. In a front-end process, the STD engine is used to preindex target spoken documents from a keyword list built from an automatic speech recognition result. The STD result includes a set of keywords and their detection intervals (positions) in the spoken documents. For keywords having competitive intervals, we rank them based on the STD matching cost and select the one having the longest duration among competitive detections. The selected keywords are registered in the pre-index. They are then used to train an SVM-based classifier. In a query term search process, a query term is searched by the same STD engine, and the output candidates are verified by the SVM-based classifier. Our proposed twostage STD method with pre-indexing was evaluated using the NTCIR-10 SpokenDoc-2 STD task and it drastically outperformed the traditional STD method based on dynamic time warping and a confusion network-based index.

show abstract

Section: Introductionmentioning

confidence: 99%

Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords

Domoto

Utsuro

Sawada

et al. 2016

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Our approach has been evaluated on the same OOV subset as reported already in the previous paper [17]. In addition, it has also been evaluated on the spoken query (SQ)-STD subtask of the NTCIR-11 SpokenQuery&Doc-1 task [18], [19], which is different from the task we use in this study.…”

Section: Related Workmentioning

confidence: 99%

Re-Ranking Approach of Spoken Term Detection Using Conditional Random Fields-Based Triphone Detection

Sawada

Nishizaki

2016

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

SUMMARY This study proposes a two-pass spoken term detection (STD) method. The first pass uses a phoneme-based dynamic time warping (DTW)-based STD, and the second pass recomputes detection scores produced by the first pass using conditional random fields (CRF)-based triphone detectors. In the second-pass, we treat STD as a sequence labeling problem. We use CRF-based triphone detection models based on features generated from multiple types of phoneme-based transcriptions. The models train recognition error patterns such as phoneme-to-phoneme confusions in the CRF framework. Consequently, the models can detect a triphone comprising a query term with a detection probability. In the experimental evaluation of two types of test collections, the CRF-based approach worked well in the re-ranking process for the DTW-based detections. CRF-based re-ranking showed 2.1% and 2.0% absolute improvements in F-measure for each of the two test collections.

show abstract

Re-ranking of spoken term detections using CRF-based triphone detection models

Cited by 2 publications

References 11 publications

Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords

Spoken Term Detection Using SVM-Based Classifier Trained with Pre-Indexed Keywords

Re-Ranking Approach of Spoken Term Detection Using Conditional Random Fields-Based Triphone Detection

Contact Info

Product

Resources

About