Research on LSTM+Attention Model of Infant Cry Classification

Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are u...
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Autor*in:	Jian, Tianye [verfasserIn] Peng, Yizhun Peng, Wanlong Yang, Zhou

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Children’s emotional time—sequence relationship frame-level speech feature deep attention gate long- and short-term memory (LSTM)

Anmerkung:	© The Authors 2021

Übergeordnetes Werk:	Enthalten in: Journal of Robotics, Networking and Artificial Life - Springer Netherlands, 2014, 8(2021), 3 vom: 09. Okt., Seite 218-223
Übergeordnetes Werk:	volume:8 ; year:2021 ; number:3 ; day:09 ; month:10 ; pages:218-223

Links:	Volltext

DOI / URN:	10.2991/jrnal.k.210922.013

Katalog-ID:	SPR055096832

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520			\|a Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher.
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allfields	10.2991/jrnal.k.210922.013 doi (DE-627)SPR055096832 (SPR)jrnal.k.210922.013-e DE-627 ger DE-627 rakwb eng Jian, Tianye verfasserin aut Research on LSTM+Attention Model of Infant Cry Classification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2021 Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. Children’s emotional (dpeaa)DE-He213 time—sequence relationship (dpeaa)DE-He213 frame-level speech feature (dpeaa)DE-He213 deep attention gate (dpeaa)DE-He213 long- and short-term memory (LSTM) (dpeaa)DE-He213 Peng, Yizhun aut Peng, Wanlong aut Yang, Zhou aut Enthalten in Journal of Robotics, Networking and Artificial Life Springer Netherlands, 2014 8(2021), 3 vom: 09. Okt., Seite 218-223 (DE-627)1005552487 (DE-600)2912430-X 2352-6386 nnns volume:8 year:2021 number:3 day:09 month:10 pages:218-223 https://dx.doi.org/10.2991/jrnal.k.210922.013 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 3 09 10 218-223
spelling	10.2991/jrnal.k.210922.013 doi (DE-627)SPR055096832 (SPR)jrnal.k.210922.013-e DE-627 ger DE-627 rakwb eng Jian, Tianye verfasserin aut Research on LSTM+Attention Model of Infant Cry Classification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2021 Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. Children’s emotional (dpeaa)DE-He213 time—sequence relationship (dpeaa)DE-He213 frame-level speech feature (dpeaa)DE-He213 deep attention gate (dpeaa)DE-He213 long- and short-term memory (LSTM) (dpeaa)DE-He213 Peng, Yizhun aut Peng, Wanlong aut Yang, Zhou aut Enthalten in Journal of Robotics, Networking and Artificial Life Springer Netherlands, 2014 8(2021), 3 vom: 09. Okt., Seite 218-223 (DE-627)1005552487 (DE-600)2912430-X 2352-6386 nnns volume:8 year:2021 number:3 day:09 month:10 pages:218-223 https://dx.doi.org/10.2991/jrnal.k.210922.013 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 3 09 10 218-223
allfields_unstemmed	10.2991/jrnal.k.210922.013 doi (DE-627)SPR055096832 (SPR)jrnal.k.210922.013-e DE-627 ger DE-627 rakwb eng Jian, Tianye verfasserin aut Research on LSTM+Attention Model of Infant Cry Classification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2021 Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. Children’s emotional (dpeaa)DE-He213 time—sequence relationship (dpeaa)DE-He213 frame-level speech feature (dpeaa)DE-He213 deep attention gate (dpeaa)DE-He213 long- and short-term memory (LSTM) (dpeaa)DE-He213 Peng, Yizhun aut Peng, Wanlong aut Yang, Zhou aut Enthalten in Journal of Robotics, Networking and Artificial Life Springer Netherlands, 2014 8(2021), 3 vom: 09. Okt., Seite 218-223 (DE-627)1005552487 (DE-600)2912430-X 2352-6386 nnns volume:8 year:2021 number:3 day:09 month:10 pages:218-223 https://dx.doi.org/10.2991/jrnal.k.210922.013 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 3 09 10 218-223
allfieldsGer	10.2991/jrnal.k.210922.013 doi (DE-627)SPR055096832 (SPR)jrnal.k.210922.013-e DE-627 ger DE-627 rakwb eng Jian, Tianye verfasserin aut Research on LSTM+Attention Model of Infant Cry Classification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2021 Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. Children’s emotional (dpeaa)DE-He213 time—sequence relationship (dpeaa)DE-He213 frame-level speech feature (dpeaa)DE-He213 deep attention gate (dpeaa)DE-He213 long- and short-term memory (LSTM) (dpeaa)DE-He213 Peng, Yizhun aut Peng, Wanlong aut Yang, Zhou aut Enthalten in Journal of Robotics, Networking and Artificial Life Springer Netherlands, 2014 8(2021), 3 vom: 09. Okt., Seite 218-223 (DE-627)1005552487 (DE-600)2912430-X 2352-6386 nnns volume:8 year:2021 number:3 day:09 month:10 pages:218-223 https://dx.doi.org/10.2991/jrnal.k.210922.013 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 3 09 10 218-223
allfieldsSound	10.2991/jrnal.k.210922.013 doi (DE-627)SPR055096832 (SPR)jrnal.k.210922.013-e DE-627 ger DE-627 rakwb eng Jian, Tianye verfasserin aut Research on LSTM+Attention Model of Infant Cry Classification 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Authors 2021 Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. Children’s emotional (dpeaa)DE-He213 time—sequence relationship (dpeaa)DE-He213 frame-level speech feature (dpeaa)DE-He213 deep attention gate (dpeaa)DE-He213 long- and short-term memory (LSTM) (dpeaa)DE-He213 Peng, Yizhun aut Peng, Wanlong aut Yang, Zhou aut Enthalten in Journal of Robotics, Networking and Artificial Life Springer Netherlands, 2014 8(2021), 3 vom: 09. Okt., Seite 218-223 (DE-627)1005552487 (DE-600)2912430-X 2352-6386 nnns volume:8 year:2021 number:3 day:09 month:10 pages:218-223 https://dx.doi.org/10.2991/jrnal.k.210922.013 kostenfrei Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2021 3 09 10 218-223
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source	Enthalten in Journal of Robotics, Networking and Artificial Life 8(2021), 3 vom: 09. Okt., Seite 218-223 volume:8 year:2021 number:3 day:09 month:10 pages:218-223
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author	Jian, Tianye
spellingShingle	Jian, Tianye misc Children’s emotional misc time—sequence relationship misc frame-level speech feature misc deep attention gate misc long- and short-term memory (LSTM) Research on LSTM+Attention Model of Infant Cry Classification
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topic_title	Research on LSTM+Attention Model of Infant Cry Classification Children’s emotional (dpeaa)DE-He213 time—sequence relationship (dpeaa)DE-He213 frame-level speech feature (dpeaa)DE-He213 deep attention gate (dpeaa)DE-He213 long- and short-term memory (LSTM) (dpeaa)DE-He213
topic	misc Children’s emotional misc time—sequence relationship misc frame-level speech feature misc deep attention gate misc long- and short-term memory (LSTM)
topic_unstemmed	misc Children’s emotional misc time—sequence relationship misc frame-level speech feature misc deep attention gate misc long- and short-term memory (LSTM)
topic_browse	misc Children’s emotional misc time—sequence relationship misc frame-level speech feature misc deep attention gate misc long- and short-term memory (LSTM)
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title	Research on LSTM+Attention Model of Infant Cry Classification
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title_full	Research on LSTM+Attention Model of Infant Cry Classification
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author_browse	Jian, Tianye Peng, Yizhun Peng, Wanlong Yang, Zhou
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title_sort	research on lstm+attention model of infant cry classification
title_auth	Research on LSTM+Attention Model of Infant Cry Classification
abstract	Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. © The Authors 2021
abstractGer	Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. © The Authors 2021
abstract_unstemmed	Abstract According to the different emotional needs of infants, the effective acquisition of frame-level speech features is realized, and the infant speech emotion recognition model based on the improved Long- and Short-Term Memory (LSTM) network is established. The frame-level speech features are used instead of the traditional statistical features to preserve the temporal relationships in the original speech, and the traditional forgetting and input gates are transformed into attention gates by introducing an attention mechanism, to improve the performance of speech emotion recognition, the depth attention gate is calculated according to the self-defined depth strategy. The results show that, in Fau Aibo Children’s emotional data corpus and baby crying emotional needs database, compared with the traditional LSTM based model, the recall rate and F1-score of this model are 3.14%, 5.50%, 1.84% and 5.49% higher, respectively, compared with the traditional model based on LSTM and gated recurrent unit, the training time is shorter and the speech emotion recognition rate of baby is higher. © The Authors 2021
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title_short	Research on LSTM+Attention Model of Infant Cry Classification
url	https://dx.doi.org/10.2991/jrnal.k.210922.013
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author2	Peng, Yizhun Peng, Wanlong Yang, Zhou
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up_date	2024-07-04T04:09:00.631Z
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