Academic Journal
Speech to text converter for Filipino language using hybrid artificial neural network/Hidden Markov Model
| Title: | Speech to text converter for Filipino language using hybrid artificial neural network/Hidden Markov Model |
|---|---|
| Authors: | Chan, Aylmer Jason L., Hatulan, Roger John F., Hilario, Apolonio D., Jr., Lim, Johann Kenneth T. |
| Source: | Bachelor's Theses |
| Publisher Information: | Animo Repository |
| Publication Year: | 2007 |
| Subject Terms: | Automatic speech recognition, Speech processing systems--Computer programs, lang |
| Description: | The Filipino language is a simple yet at the same time a complex language with its semantics and grammar syntax relatively easy to learn for a person. However for a machine or computer to learn this kind of capacity for language recognition require a moderately complex system. The basis for this thesis project stems from the need of convenience for the handicapped people interacting with computer and machines alike. The rapid change in the development and evolution of speech recognition systems make this endeavor a significant step for the Filipino language industry. The thesis aims to make a speech recognition system which utilizes speech processing techniques to evaluate certain words spoken in Filipino. The group first employs feature extraction as the front end process of the speech recognition system then experiments with different algorithm techniques to for optimization by using either a feed-forward back propagation algorithm or SOM networks to train the samples for the neural networks. The samples obtained from the UP Speech Corpus are to be segmented by phonemes. For the actual system, input way files undergo the speech process module for the translation of the inputs into frames and are fed to the word segmentation module which then goes to a feature extraction module. The feature extraction module computes for feature vectors which would serve as inputs to the neural network. After training the networks to a specified target, their outputs would then be used as inputs to the probabilistic Hidden Markov Model [HMM] which would then predict the most possible sequence of outputs, in this case, the phoneme sequence. A decoder would then translate the phoneme sequence into a sequence of letters that form the word used in the lookup table to search for the best likely match of the recognized word. |
| Document Type: | text |
| Language: | English |
| Relation: | https://animorepository.dlsu.edu.ph/etd_bachelors/6016 |
| Availability: | https://animorepository.dlsu.edu.ph/etd_bachelors/6016 |
| Rights: | undefined |
| Accession Number: | edsbas.679943D3 |
| Database: | BASE |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://animorepository.dlsu.edu.ph/etd_bachelors/6016# Name: EDS - BASE (ns324271) Category: fullText Text: View record from BASE |
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| Items | – Name: Title Label: Title Group: Ti Data: Speech to text converter for Filipino language using hybrid artificial neural network/Hidden Markov Model – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Chan%2C+Aylmer+Jason+L%2E%22">Chan, Aylmer Jason L.</searchLink><br /><searchLink fieldCode="AR" term="%22Hatulan%2C+Roger+John+F%2E%22">Hatulan, Roger John F.</searchLink><br /><searchLink fieldCode="AR" term="%22Hilario%2C+Apolonio+D%2E%2C+Jr%2E%22">Hilario, Apolonio D., Jr.</searchLink><br /><searchLink fieldCode="AR" term="%22Lim%2C+Johann+Kenneth+T%2E%22">Lim, Johann Kenneth T.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Bachelor's Theses – Name: Publisher Label: Publisher Information Group: PubInfo Data: Animo Repository – Name: DatePubCY Label: Publication Year Group: Date Data: 2007 – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Automatic+speech+recognition%22">Automatic speech recognition</searchLink><br /><searchLink fieldCode="DE" term="%22Speech+processing+systems--Computer+programs%22">Speech processing systems--Computer programs</searchLink><br /><searchLink fieldCode="DE" term="%22lang%22">lang</searchLink> – Name: Abstract Label: Description Group: Ab Data: The Filipino language is a simple yet at the same time a complex language with its semantics and grammar syntax relatively easy to learn for a person. However for a machine or computer to learn this kind of capacity for language recognition require a moderately complex system. The basis for this thesis project stems from the need of convenience for the handicapped people interacting with computer and machines alike. The rapid change in the development and evolution of speech recognition systems make this endeavor a significant step for the Filipino language industry. The thesis aims to make a speech recognition system which utilizes speech processing techniques to evaluate certain words spoken in Filipino. The group first employs feature extraction as the front end process of the speech recognition system then experiments with different algorithm techniques to for optimization by using either a feed-forward back propagation algorithm or SOM networks to train the samples for the neural networks. The samples obtained from the UP Speech Corpus are to be segmented by phonemes. For the actual system, input way files undergo the speech process module for the translation of the inputs into frames and are fed to the word segmentation module which then goes to a feature extraction module. The feature extraction module computes for feature vectors which would serve as inputs to the neural network. After training the networks to a specified target, their outputs would then be used as inputs to the probabilistic Hidden Markov Model [HMM] which would then predict the most possible sequence of outputs, in this case, the phoneme sequence. A decoder would then translate the phoneme sequence into a sequence of letters that form the word used in the lookup table to search for the best likely match of the recognized word. – Name: TypeDocument Label: Document Type Group: TypDoc Data: text – Name: Language Label: Language Group: Lang Data: English – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://animorepository.dlsu.edu.ph/etd_bachelors/6016 – Name: URL Label: Availability Group: URL Data: https://animorepository.dlsu.edu.ph/etd_bachelors/6016 – Name: Copyright Label: Rights Group: Cpyrght Data: undefined – Name: AN Label: Accession Number Group: ID Data: edsbas.679943D3 |
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| RecordInfo | BibRecord: BibEntity: Languages: – Text: English Subjects: – SubjectFull: Automatic speech recognition Type: general – SubjectFull: Speech processing systems--Computer programs Type: general – SubjectFull: lang Type: general Titles: – TitleFull: Speech to text converter for Filipino language using hybrid artificial neural network/Hidden Markov Model Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Chan, Aylmer Jason L. – PersonEntity: Name: NameFull: Hatulan, Roger John F. – PersonEntity: Name: NameFull: Hilario, Apolonio D., Jr. – PersonEntity: Name: NameFull: Lim, Johann Kenneth T. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2007 Identifiers: – Type: issn-locals Value: edsbas Titles: – TitleFull: Bachelor's Theses Type: main |
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