Using phonetic feature extraction to determine optimal speech regions for maximising the effectiveness of glottal source analysis

被引：0

作者：

Kane, John ^{[1
]}

Yanushevskaya, Irena ^{[1
]}

Dalton, John ^{[1
]}

Gobl, Christer ^{[1
]}

Chasaide, Ailbhe Ni ^{[1
]}

机构：

[1] Trinity Coll Dublin, Sch Linguist Speech & Commun Sci, Phonet & Speech Lab, Dublin, Ireland

来源：

14TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2013), VOLS 1-5 | 2013年

基金：

爱尔兰科学基金会;

关键词：

Glottal source; voice source; phonetic features; voice quality;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Parameterisation of the glottal source has become increasingly useful for speech technology. For many applications it may be desirable to restrict the glottal source feature data to only speech regions where it can be reliably extracted. In this paper we exploit the previously proposed set of binary phonetic feature extractors to help determine optimal regions for glottal source analysis. Besides validation of the phonetic feature extractors, we also quantitatively assess their usefulness for improving voice quality classification and find highly significant reductions in error rates in particular when nasals and fricative regions are excluded.

引用

页码：29 / 33

页数：5

共 21 条

[1] Phonetic feature extraction for context-sensitive glottal source processing
Kane, John
Aylett, Matthew
Yanushevskaya, Irena
Gobl, Christer
SPEECH COMMUNICATION, 2014, 59 : 10 - 21
[2] Glottal instants extraction from speech signal using Deep Feature Loss
Shetty, Supritha M.
Durgesht, Suraj
Deepak, K. T.
2022 IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATIONS, SPCOM, 2022,
[3] Reconstruction Of Speech Signal Using Empirical Mode Decomposition Based Glottal Source Extraction
Goswami, Nisha
Sarma, Mousmita
Sarma, Kandarpa Kumar
2013 1ST INTERNATIONAL CONFERENCE ON EMERGING TRENDS AND APPLICATIONS IN COMPUTER SCIENCE (ICETACS), 2013, : 27 - 32
[4] Speech analysis and feature extraction using chaotic models
Pitsikalis, V
Maragos, P
2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS, 2002, : 533 - 536
[5] Speech feature extraction using independent component analysis
Lee, JH
Jung, HY
Lee, TW
Lee, SY
2000 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, PROCEEDINGS, VOLS I-VI, 2000, : 1631 - 1634
[6] Pronunciation modeling of spontaneous mandarin speech using phonetic feature distance and optimal Gaussian mixture sharing
Yi, L
Pascale, F
Byrne, W
Umar, R
2001 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-VI, PROCEEDINGS: VOL I: SPEECH PROCESSING 1; VOL II: SPEECH PROCESSING 2 IND TECHNOL TRACK DESIGN & IMPLEMENTATION OF SIGNAL PROCESSING SYSTEMS NEURALNETWORKS FOR SIGNAL PROCESSING; VOL III: IMAGE & MULTIDIMENSIONAL SIGNAL PROCESSING MULTIMEDIA SIGNAL PROCESSING - VOL IV: SIGNAL PROCESSING FOR COMMUNICATIONS; VOL V: SIGNAL PROCESSING EDUCATION SENSOR ARRAY & MULTICHANNEL SIGNAL PROCESSING AUDIO & ELECTROACOUSTICS; VOL VI: SIGNAL PROCESSING THEORY & METHODS STUDENT FORUM, 2001, : 4019 - 4019
[7] Investigating voice function characteristics of Greek speakers with hearing loss using automatic glottal source feature extraction
Sfakianaki, Anna
Kafentzis, George P.
INTERSPEECH 2021, 2021, : 1394 - 1398
[8] On glottal source shape parameter transformation using a novel deterministic and stochastic speech analysis and synthesis system
Huber, Stefan
Roebel, Axel
16TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2015), VOLS 1-5, 2015, : 289 - 293
[9] Feature extraction and classification efficiency analysis using machine learning approach for speech signal
Singh, Mahesh K.
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 83 (16) : 47069 - 47084
[10] Feature extraction and classification efficiency analysis using machine learning approach for speech signal
Mahesh K. Singh
Multimedia Tools and Applications, 2024, 83 : 47069 - 47084

← 1 2 3 →