Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract

Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction te...
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Autor*in:	Javed, Muhammad [verfasserIn] Baig, Mirza Muhammad Ali [verfasserIn] Qazi, Saad Ahmed [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Automatic speech segmentation Implicit segmentation Phonetic alignment Classical Arabic (CA) The Holy Quran (THQ)

Übergeordnetes Werk:	Enthalten in: The Arabian journal for science and engineering - Berlin : Springer, 2011, 45(2019), 3 vom: 22. Aug., Seite 1581-1597
Übergeordnetes Werk:	volume:45 ; year:2019 ; number:3 ; day:22 ; month:08 ; pages:1581-1597

Links:	Volltext

DOI / URN:	10.1007/s13369-019-04065-5

Katalog-ID:	SPR032078382

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520			\|a Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation.
650		4	\|a Automatic speech segmentation \|7 (dpeaa)DE-He213
650		4	\|a Implicit segmentation \|7 (dpeaa)DE-He213
650		4	\|a Phonetic alignment \|7 (dpeaa)DE-He213
650		4	\|a Classical Arabic (CA) \|7 (dpeaa)DE-He213
650		4	\|a The Holy Quran (THQ) \|7 (dpeaa)DE-He213
700	1		\|a Baig, Mirza Muhammad Ali \|e verfasserin \|4 aut
700	1		\|a Qazi, Saad Ahmed \|e verfasserin \|4 aut
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allfields	10.1007/s13369-019-04065-5 doi (DE-627)SPR032078382 (SPR)s13369-019-04065-5-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Javed, Muhammad verfasserin aut Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation. Automatic speech segmentation (dpeaa)DE-He213 Implicit segmentation (dpeaa)DE-He213 Phonetic alignment (dpeaa)DE-He213 Classical Arabic (CA) (dpeaa)DE-He213 The Holy Quran (THQ) (dpeaa)DE-He213 Baig, Mirza Muhammad Ali verfasserin aut Qazi, Saad Ahmed verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2019), 3 vom: 22. Aug., Seite 1581-1597 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597 https://dx.doi.org/10.1007/s13369-019-04065-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2019 3 22 08 1581-1597
spelling	10.1007/s13369-019-04065-5 doi (DE-627)SPR032078382 (SPR)s13369-019-04065-5-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Javed, Muhammad verfasserin aut Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation. Automatic speech segmentation (dpeaa)DE-He213 Implicit segmentation (dpeaa)DE-He213 Phonetic alignment (dpeaa)DE-He213 Classical Arabic (CA) (dpeaa)DE-He213 The Holy Quran (THQ) (dpeaa)DE-He213 Baig, Mirza Muhammad Ali verfasserin aut Qazi, Saad Ahmed verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2019), 3 vom: 22. Aug., Seite 1581-1597 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597 https://dx.doi.org/10.1007/s13369-019-04065-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2019 3 22 08 1581-1597
allfields_unstemmed	10.1007/s13369-019-04065-5 doi (DE-627)SPR032078382 (SPR)s13369-019-04065-5-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Javed, Muhammad verfasserin aut Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation. Automatic speech segmentation (dpeaa)DE-He213 Implicit segmentation (dpeaa)DE-He213 Phonetic alignment (dpeaa)DE-He213 Classical Arabic (CA) (dpeaa)DE-He213 The Holy Quran (THQ) (dpeaa)DE-He213 Baig, Mirza Muhammad Ali verfasserin aut Qazi, Saad Ahmed verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2019), 3 vom: 22. Aug., Seite 1581-1597 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597 https://dx.doi.org/10.1007/s13369-019-04065-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2019 3 22 08 1581-1597
allfieldsGer	10.1007/s13369-019-04065-5 doi (DE-627)SPR032078382 (SPR)s13369-019-04065-5-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Javed, Muhammad verfasserin aut Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation. Automatic speech segmentation (dpeaa)DE-He213 Implicit segmentation (dpeaa)DE-He213 Phonetic alignment (dpeaa)DE-He213 Classical Arabic (CA) (dpeaa)DE-He213 The Holy Quran (THQ) (dpeaa)DE-He213 Baig, Mirza Muhammad Ali verfasserin aut Qazi, Saad Ahmed verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2019), 3 vom: 22. Aug., Seite 1581-1597 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597 https://dx.doi.org/10.1007/s13369-019-04065-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2019 3 22 08 1581-1597
allfieldsSound	10.1007/s13369-019-04065-5 doi (DE-627)SPR032078382 (SPR)s13369-019-04065-5-e DE-627 ger DE-627 rakwb eng 600 500 ASE 31.00 bkl Javed, Muhammad verfasserin aut Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation. Automatic speech segmentation (dpeaa)DE-He213 Implicit segmentation (dpeaa)DE-He213 Phonetic alignment (dpeaa)DE-He213 Classical Arabic (CA) (dpeaa)DE-He213 The Holy Quran (THQ) (dpeaa)DE-He213 Baig, Mirza Muhammad Ali verfasserin aut Qazi, Saad Ahmed verfasserin aut Enthalten in The Arabian journal for science and engineering Berlin : Springer, 2011 45(2019), 3 vom: 22. Aug., Seite 1581-1597 (DE-627)588780731 (DE-600)2471504-9 2191-4281 nnns volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597 https://dx.doi.org/10.1007/s13369-019-04065-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 31.00 ASE AR 45 2019 3 22 08 1581-1597
language	English
source	Enthalten in The Arabian journal for science and engineering 45(2019), 3 vom: 22. Aug., Seite 1581-1597 volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597
sourceStr	Enthalten in The Arabian journal for science and engineering 45(2019), 3 vom: 22. Aug., Seite 1581-1597 volume:45 year:2019 number:3 day:22 month:08 pages:1581-1597
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Automatic speech segmentation Implicit segmentation Phonetic alignment Classical Arabic (CA) The Holy Quran (THQ)
dewey-raw	600
isfreeaccess_bool	false
container_title	The Arabian journal for science and engineering
authorswithroles_txt_mv	Javed, Muhammad @@aut@@ Baig, Mirza Muhammad Ali @@aut@@ Qazi, Saad Ahmed @@aut@@
publishDateDaySort_date	2019-08-22T00:00:00Z
hierarchy_top_id	588780731
dewey-sort	3600
id	SPR032078382
language_de	englisch
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author	Javed, Muhammad
spellingShingle	Javed, Muhammad ddc 600 bkl 31.00 misc Automatic speech segmentation misc Implicit segmentation misc Phonetic alignment misc Classical Arabic (CA) misc The Holy Quran (THQ) Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract
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topic_title	600 500 ASE 31.00 bkl Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract Automatic speech segmentation (dpeaa)DE-He213 Implicit segmentation (dpeaa)DE-He213 Phonetic alignment (dpeaa)DE-He213 Classical Arabic (CA) (dpeaa)DE-He213 The Holy Quran (THQ) (dpeaa)DE-He213
topic	ddc 600 bkl 31.00 misc Automatic speech segmentation misc Implicit segmentation misc Phonetic alignment misc Classical Arabic (CA) misc The Holy Quran (THQ)
topic_unstemmed	ddc 600 bkl 31.00 misc Automatic speech segmentation misc Implicit segmentation misc Phonetic alignment misc Classical Arabic (CA) misc The Holy Quran (THQ)
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title	Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract
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title_full	Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract
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author_browse	Javed, Muhammad Baig, Mirza Muhammad Ali Qazi, Saad Ahmed
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title_sort	unsupervised phonetic segmentation of classical arabic speech using forward and inverse characteristics of the vocal tract
title_auth	Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract
abstract	Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation.
abstractGer	Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation.
abstract_unstemmed	Abstract Automatic segmentation of speech is about identifying boundaries of phonemes in a given utterance. This paper presents a strategy driven by cosine distance similarity scores for identifying phoneme boundaries. The proposed strategy helps in the selection of appropriate feature extraction technique for speech segmentation applications. After assessing various state-of-the-art speech processing techniques, a novel combination of forward and inverse characteristics of vocal tract (FICV) is developed. The proposed technique is evaluated on Classical Arabic dataset. Extensive experiments are made to compare the proposed technique with state-of-the-art techniques, including the hidden Markov model-based forced alignment procedures. The results show that proposed technique has total error rate of 14.48%, while the accuracy is 85.2% within 10 ms alignment error. When compared with the existing state-of-the-art technique, the proposed technique outperforms by 12.29% and 22.73% in terms of error rates and alignment accuracies, respectively, which signifies the potential of using FICV in speech segmentation.
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title_short	Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract
url	https://dx.doi.org/10.1007/s13369-019-04065-5
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author2	Baig, Mirza Muhammad Ali Qazi, Saad Ahmed
author2Str	Baig, Mirza Muhammad Ali Qazi, Saad Ahmed
ppnlink	588780731
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doi_str	10.1007/s13369-019-04065-5
up_date	2024-07-04T02:22:09.615Z
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Unsupervised Phonetic Segmentation of Classical Arabic Speech Using Forward and Inverse Characteristics of the Vocal Tract

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