Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems
Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR syst...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kammili, Pavan Raju [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: International journal of speech technology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 25(2021), 1 vom: 20. Sept., Seite 197-204 |
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| Übergeordnetes Werk: |
volume:25 ; year:2021 ; number:1 ; day:20 ; month:09 ; pages:197-204 |
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| DOI / URN: |
10.1007/s10772-021-09897-x |
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| Katalog-ID: |
SPR046489363 |
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| 520 | |a Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. | ||
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| 700 | 1 | |a Ramakrishnam Raju, B. H. V. S. |4 aut | |
| 700 | 1 | |a Krishna, A. Sri |4 aut | |
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10.1007/s10772-021-09897-x doi (DE-627)SPR046489363 (SPR)s10772-021-09897-x-e DE-627 ger DE-627 rakwb eng Kammili, Pavan Raju verfasserin (orcid)0000-0002-9276-0511 aut Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. ASR (dpeaa)DE-He213 Prosody (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Ramakrishnam Raju, B. H. V. S. aut Krishna, A. Sri aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 1 vom: 20. Sept., Seite 197-204 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:1 day:20 month:09 pages:197-204 https://dx.doi.org/10.1007/s10772-021-09897-x 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 1 20 09 197-204 |
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10.1007/s10772-021-09897-x doi (DE-627)SPR046489363 (SPR)s10772-021-09897-x-e DE-627 ger DE-627 rakwb eng Kammili, Pavan Raju verfasserin (orcid)0000-0002-9276-0511 aut Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. ASR (dpeaa)DE-He213 Prosody (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Ramakrishnam Raju, B. H. V. S. aut Krishna, A. Sri aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 1 vom: 20. Sept., Seite 197-204 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:1 day:20 month:09 pages:197-204 https://dx.doi.org/10.1007/s10772-021-09897-x 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 1 20 09 197-204 |
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10.1007/s10772-021-09897-x doi (DE-627)SPR046489363 (SPR)s10772-021-09897-x-e DE-627 ger DE-627 rakwb eng Kammili, Pavan Raju verfasserin (orcid)0000-0002-9276-0511 aut Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. ASR (dpeaa)DE-He213 Prosody (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Ramakrishnam Raju, B. H. V. S. aut Krishna, A. Sri aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 1 vom: 20. Sept., Seite 197-204 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:1 day:20 month:09 pages:197-204 https://dx.doi.org/10.1007/s10772-021-09897-x 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 1 20 09 197-204 |
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10.1007/s10772-021-09897-x doi (DE-627)SPR046489363 (SPR)s10772-021-09897-x-e DE-627 ger DE-627 rakwb eng Kammili, Pavan Raju verfasserin (orcid)0000-0002-9276-0511 aut Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. ASR (dpeaa)DE-He213 Prosody (dpeaa)DE-He213 Data augmentation (dpeaa)DE-He213 Ramakrishnam Raju, B. H. V. S. aut Krishna, A. Sri aut Enthalten in International journal of speech technology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 25(2021), 1 vom: 20. Sept., Seite 197-204 (DE-627)27134895X (DE-600)1479775-6 1572-8110 nnns volume:25 year:2021 number:1 day:20 month:09 pages:197-204 https://dx.doi.org/10.1007/s10772-021-09897-x 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_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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_2190 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_4126 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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 25 2021 1 20 09 197-204 |
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handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained asr systems |
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Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems |
| abstract |
Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstractGer |
Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract In this paper, the effect of emotional speech on the performance of neutral speech trained ASR systems is studied. Prosody-modification based data augmentation is explored to compensate the affected ASR performance due to emotional speech. The primary motive is to develop an Telugu ASR system that is least affected by these emotion based intrinsic speaker related acoustic variations. Two factors contributing towards the intrinsic speaker related variability that are focused in this research are the fundamental frequency [%$(F_0)%$ or pitch] and the speaking rate variations. To simulate ASR task, we performed the training of our ASR system on neutral speech and tested it for data from emotional as well as neutral speech. Compared to the performance metrics of neutral speech at testing stage, emotional speech performance metrics are extremely degraded. This performance degradation is observed due to the difference in the prosody and speaking rate parameters of neutral and emotional speech. To overcome this performance degradation problem, prosody and speaking rate parameters are varied and modified to create the newer augmented versions of the training data. The original and augmented versions of the training data are pooled together and re-trained in order to capture greater emotion-specific variations. For the Telugu ASR experiments, we used Microsoft speech corpus for Indian languages(MSC-IL) for training neutral speech and Indian Institute of Technology Kharagpur Simulated Emotion Speech Corpus (IITKGP-SESC) for evaluating emotional speech. The basic emotions of anger, happiness and sad are considered for evaluation along with neutral speech. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Handling emotional speech: a prosody based data augmentation technique for improving neutral speech trained ASR systems |
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https://dx.doi.org/10.1007/s10772-021-09897-x |
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Ramakrishnam Raju, B. H. V. S. Krishna, A. Sri |
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Ramakrishnam Raju, B. H. V. S. Krishna, A. Sri |
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10.1007/s10772-021-09897-x |
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2024-07-03T22:51:09.483Z |
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| score |
7.398814 |