1. bookVolumen 73 (2022): Heft 5 (September 2022)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1339-309X
Erstveröffentlichung
07 Jun 2011
Erscheinungsweise
6 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

Comparison of methods for determining speech voicing based on tests performed on paired consonants and continuous speech

Online veröffentlicht: 15 Nov 2022
Volumen & Heft: Volumen 73 (2022) - Heft 5 (September 2022)
Seitenbereich: 359 - 362
Eingereicht: 08 Sep 2022
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
1339-309X
Erstveröffentlichung
07 Jun 2011
Erscheinungsweise
6 Hefte pro Jahr
Sprachen
Englisch

[1] International Phonetic Association, Handbook of the International Phonetic Association A Guide to the Use of the International Phonetic Alphabet, Cambridge University Press, 1999. Search in Google Scholar

[2] https://www.internationalphoneticassociation.org/IPAcharts/inter_chart_2018/IPA_2018.html. Search in Google Scholar

[3] R. Sussex and P. Cubberley, The Slavic Languages, Cambridge University Press, 2006.10.1017/CBO9780511486807 Search in Google Scholar

[4] D. Odden, Introducing Phonology, New York, Cambridge University Press, 2005.10.1017/CBO9780511808869 Search in Google Scholar

[5] M. J. Ball, S. J. Howard, and K. Miller, “Revisions to the ExtIPA,”, Journal of the International Phonetic Association, pp. 156–164. Search in Google Scholar

[6] M. Sigmund, A. Prokes, and Z. Brabec, “Statistical Analysis of Glottal Pulses in Speech under Psychological Stress,”, 16th European Signal Processing Conference (EUSIPCO), pp. 1–5, 2008. Search in Google Scholar

[7] M. M. Sondhi, “Measurement of the Glottal Waveform,”, The Journal of the Acoustical Society of America, no. 1, pp. 228–232, 1975.10.1121/1.3804291110287 Search in Google Scholar

[8] R. L. Miller, “Nature of the Vocal Cord Wave,”, The Journal of the Acoustical Society of America, no. 6, pp. 667–677, 1959.10.1121/1.1907771 Search in Google Scholar

[9] J. Psutka, L. Müller, J. Matoušek, and V. Radová, Mluvíme s počítačem česky, Prague: Academia, 2006, (in Czech). Search in Google Scholar

[10] R. G. Bachu, S. Kopparthi, B. Adapa, and B. D. Barkana, “Voiced/unvoiced Decision for Speech Signals Based on Zero Crossing Rate and Energy,”, Advanced Techniques in Computing Sciences and Software Engineering, Dordrecht: Springer, pp. 279–282, 2010.10.1007/978-90-481-3660-5_47 Search in Google Scholar

[11] J. Heranová, Harmonicita jako možný indiktor hranic mezi segmenty v češtině, Praha, Univerzita Karlova, Filozofická fakulta, Fonetickýústav, 2010, (in Czech). Search in Google Scholar

[12] L. R. Rabiner and R. W. Schafer, Theory and Applications of Digital Speech Processing, London: Prentice Hall, 2011. Search in Google Scholar

[13] H. Misra, S. Ikbal, H. Bourlard, and H. Hermansky, “Spectral Entropy Based Feature for Robust ASR,”, Int. Conference on Acoustics, Speech, and Signal Processing, pp. I193–I196, 2004. Search in Google Scholar

[14] P. J. Murphy and O. O. Akande, “Noise Estimation in Voice Signals Using Short-Term Cepstral Analysis,”, The Journal of the Acoustical Society of America, no. 3, pp. 1679–1690, 2007. Search in Google Scholar

[15] Y. Wei, Y. Zeng, and C. Li, “Single-Channel Speech Enhancement Based on Subband Spectral Entropy,”, Journal of the Audio Engineering Society, no. 3, pp. 100–113, 2018.10.17743/jaes.2018.0003 Search in Google Scholar

[16] M. Sigmund, “Statistical Analysis of Fundamental Frequency Based Features in Speech Under Stress”, Information Technology and Control, no. 3, pp. 286–291, 2013.10.5755/j01.itc.42.3.3895 Search in Google Scholar

[17] P. Boersma and D. Weenink, “Praat: doing phonetics by computer”, http://www.praat.org, 2022. Search in Google Scholar

[18] M. Stanek and M. Sigmund, “Psychological Stress Detection in Speech Using Return-to-Opening Phase Ratios in Glottis,”, Elektronika ir Elektrotechnika, no. 5, pp. 59–63, 2015.10.5755/j01.eie.21.5.13336 Search in Google Scholar

[19] J. Pribil, A. Pribilova, and J. Matousek, “Evaluation of Synthetic Speech Quality by Statistical Analysis of Voiced and Un-voiced Part Durations,”, Int. Conference on Telecommunications and Signal Processing (TSP), pp. 1–4, 2018.10.1109/TSP.2018.8441352 Search in Google Scholar

[20] M. Sigmund and T. Dostal, “Analysis of Emotional Stress in Speech,”, International Conference on Artificial Intelligence and Applications, Innsbruck, pp. 317–322, 2004. Search in Google Scholar

[21] S. Sondhi, R. Vijay, M. Khan, and A. K. Salhan, “Voice Analysis for Detection of Deception,”, Int. Conference on Knowledge, Information and Creativity Support Systems, pp. 1–6, 2016.10.1109/KICSS.2016.7951455 Search in Google Scholar

[22] A. Bayestehtashk, M. Asgari, I. Shafran, and J. McNames, “Fully Automated Assessment of the Severity of Parkinson’s Disease from Speech,”, Computer Speech and Language, no. 1, pp. 172–185, 2015.10.1016/j.csl.2013.12.001422205425382935 Search in Google Scholar

[23] M. L. B. Pulido, J. B. A. Hernández, M. A. F. Ballester, C. M. T. González, J. Mekyska, and Z. Smékal, “Alzheimer’s Disease and Automatic Speech Analysis: A Review,”, Expert Systems with Applications, pp. 1–19, 2020.10.1016/j.eswa.2020.113213 Search in Google Scholar

[24] P. Zelinka and M. Sigmund, “Hierarchical Classification Tree Modeling of Nonstationary Noise for Robust Speech Recognition”, Information Technology and Control, no. 3, pp. 202–210, 2010. Search in Google Scholar

[25] P. Zelinka and M. Sigmund, “Automatic Vocal E ort Detection for Reliable Speech Recognition”, Int. Workshop on Machine Learning for Signal Processing, Kittila, pp. 349–354, 2010.10.1109/MLSP.2010.5589174 Search in Google Scholar

Empfohlene Artikel von Trend MD

Planen Sie Ihre Fernkonferenz mit Scienceendo