Modelling multi-level prosody and spectral features using deep neural network for an automatic tonal and non-tonal pre-classification-based Indian language identification system.
In this paper an attempt has been made to prepare an automatic tonal and non-tonal pre-classification-based Indian language identification (LID) system using multi-level prosody and spectral features. Languages are first categorized into tonal and non-tonal groups, and then, from among the languages...
| Publicado en: | Language Resources & Evaluation Vol. 55; no. 3; pp. 689 - 731 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Sep2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=151686300&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 151686300 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2021 vid: 55 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 151686300 10.1007/s10579-020-09527-z ppf: 689 ppct: 42 formats: fmt: – @attributes: type: T – @attributes: type: P size: 483KB tig: atl: Modelling multi-level prosody and spectral features using deep neural network for an automatic tonal and non-tonal pre-classification-based Indian language identification system. aug: au: China Bhanja, Chuya Laskar, Mohammad Azharuddin Laskar, Rabul Hussain affil: Department of Electronics and Communication Engineering, National Institute of Technology Silchar, 788 010, Assam, India su: Multilevel models System identification Prosodic analysis (Linguistics) Gaussian mixture models Artificial neural networks sug: subj: Multilevel models System identification Prosodic analysis (Linguistics) Gaussian mixture models Artificial neural networks keyword: Classifiers Databases Multi-level analysis Prosody and spectral features Tonal and non-tonal languages ab: In this paper an attempt has been made to prepare an automatic tonal and non-tonal pre-classification-based Indian language identification (LID) system using multi-level prosody and spectral features. Languages are first categorized into tonal and non-tonal groups, and then, from among the languages of the respective groups, individual languages are identified. The system uses syllable, word (tri-syllable) and phrase level (multi-word) prosody (collectively called multi-level prosody) along with spectral features, namely Mel-frequency cepstral coefficients (MFCCs), Mean Hilbert envelope coefficients (MHEC), and shifted delta cepstral coefficients of MFCCs and MHECs for the pre-classification task. Multi-level analysis of spectral features has also been proposed and the complementarity of the syllable, word and phrase level (spectral + prosody) has been examined for pre-classification-based LID task. Four different models, particularly, Gaussian Mixture Model (GMM)-Universal Background Model (UBM), Artificial Neural Network (ANN), i-vector based support vector machine (SVM) and Deep Neural Network (DNN) have been developed to identify the languages. Experiments have been carried out on National Institute of Technology Silchar language database (NITS-LD) and OGI Multi-language Telephone Speech corpus (OGI-MLTS). The experiments confirm that both prosody and (spectral + prosody) obtained from syllable-, word- and phrase-level carry complementary information for pre-classification-based LID task. At the pre-classification stage, DNN models based on multi-level (prosody + MFCC) features, coupled with score combination technique results in the lowest EER value of 9.6% for NITS-LD. For OGI-MLTS database, the lowest EER value of 10.2% is observed for multi-level (prosody + MHEC). The pre-classification module helps to improve the performance of baseline single-stage LID system by 3.2% and 4.2% for NITS-LD and OGI-MLTS database respectively. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2021. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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