Syllable category based short utterance speaker recognition

Fatima, Nakhat; Zheng, Thomas Fang

doi:10.1109/icalip.2012.6376657

Cited by 4 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A systematic combination of SUVN with LDA and source-normalised (SN)-LDA was further used effectively. Moreover, an alternative approach was introduced using PLDA to directly model the SUV which showed that for the combination of [44] performance evaluation JFA, i-vector CSS, WCCN, LDA, NAP, SDNAP, GPLDA [7] calibration evaluation linear calibration, cosine kernel, normalised cosine kernel 2012 [80] performance evaluation inclusion of short utterances in development data-set [51] performance evaluation an ad hoc fusion system of different TV spaces [81] evaluation of phoneme effects adding phonetic information, WCCN and EFR [82] adding of phonetic information VCs, UBVCM [83] adding of syllable information syllable categories, universal background syllable models 2013 [49] analysis on phoneme distribution score calibration with log duration as QMF, synthetic i-vectors [84] analysis of phonetic content TD-ASV, multiple enrolment, used speaker and phonetic content [85] analysis on confusion errors finding speaker-specific phonemes, formulate text using unique phonemes [21] analysis on score calibration QMFs, stacked scores, shared scaling, extrapolation [86] performance analysis TV, PLDA [87] source and utterance -dur. norm.…”

Section: I-vector Estimation and Normalisationmentioning

confidence: 99%

See 1 more Smart Citation

Speaker verification with short utterances: a review of challenges, trends and opportunities

2017

View full text Add to dashboard Cite

Automatic speaker verification (ASV) technology now reports a reasonable level of accuracy in its applications in voice-based biometric systems. However, it requires adequate amount of speech data for enrolment and verification; otherwise, the performance becomes considerably degraded. For this reason, the trade-off between the convenience and security is difficult to maintain in practical scenarios. The utterance duration remains a critical issue while deploying a voice biometric system in real-world applications. A large amount of research work has been carried out to address the limited data issue within the scope of SV. The advancements and research activities in mitigating the challenges due to short utterance have seen a significant rise in recent times. In this study, the authors present an extensive survey of SV with short utterances considering the studies from recent past and include latest research offering various solutions and analyses. The review also summarises the major findings of the studies of duration variability problem in ASV systems. Finally, they discuss a number of possible future directions promoting further research in this field. 2 Brief overview of ASV An ASV system includes three fundamental modules [1, 2]: a feature extraction unit, which transforms the speech signal in a compact form, a statistical modelling unit to characterise the extracted features, and finally a classification module to classify a test speech. 2.1 Feature extraction approaches The state-of-the-art ASV systems use three major types of feature extraction techniques: sub-segmental, segmental and suprasegmental analyses. Speech signals analysed using the frame size

show abstract

Section: I-vector Estimation and Normalisationmentioning

confidence: 99%

“…It was shown that vowels contained significant speaker discriminative information, which remained placid when vowels categorisation was deployed. A similar approach but with syllable category‐based SV in short utterance was conducted in [83].…”

Section: Research In Asv On Short Utterancesmentioning

confidence: 99%