EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS
In this experiment developed a voice recognition system using Hidden Markov Models (HMM) type of left to right with Euclidean Distance-based method to calculate the probability of observation series. Sound recordings from 10 speakers, each saying the word "SAYA" 30 times. Each voice are sp...
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2011
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| _version_ | 1797030566294454272 |
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| author | , Budi Darmawan , Dr. Eng. Ir. Risanuri Hidayat, M.Sc. |
| author_facet | , Budi Darmawan , Dr. Eng. Ir. Risanuri Hidayat, M.Sc. |
| author_sort | , Budi Darmawan |
| collection | UGM |
| description | In this experiment developed a voice recognition system using Hidden Markov Models (HMM) type of left to right with Euclidean Distance-based method to calculate the probability of observation series. Sound recordings from 10 speakers, each saying the word "SAYA" 30 times. Each voice are split according to constituent letters, and extracted using FFT and MFCC feature with a number of different coefficients. The experimental results showed that in order to obtain an average accuracy rate of 90% or more, it takes a number of MFCC coefficients by 5 coefficients per state for the number of samples 2 people, 4 coefficients per state for the number of samples 3, 4 and 5 people, 3 coefficient per state for the number samples 6 and 7 people, and 5 coefficients per state for the number of samples 8, 9 and 10 people. And to get an accuracy of 100%, it takes 5 pieces of MFCC coefficients per state for the number of samples 2 to 4 people, 12 coefficients per state for the number of samples from 5 to 9. And for a number of samples 10 people, the highest accuracy of 99% with the number of MFCC coefficients of 12 per state. The experimental results also show that the MFCC feature extraction by using the number of coefficients per state as much as 5 coefficients has to give the average accuracy is better for all of the speakers compared using FFT which takes the number of samples 256 samples per state. |
| first_indexed | 2024-03-13T22:07:49Z |
| format | Thesis |
| id | oai:generic.eprints.org:89607 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T22:07:49Z |
| publishDate | 2011 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:896072014-08-20T02:50:39Z https://repository.ugm.ac.id/89607/ EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS , Budi Darmawan , Dr. Eng. Ir. Risanuri Hidayat, M.Sc. ETD In this experiment developed a voice recognition system using Hidden Markov Models (HMM) type of left to right with Euclidean Distance-based method to calculate the probability of observation series. Sound recordings from 10 speakers, each saying the word "SAYA" 30 times. Each voice are split according to constituent letters, and extracted using FFT and MFCC feature with a number of different coefficients. The experimental results showed that in order to obtain an average accuracy rate of 90% or more, it takes a number of MFCC coefficients by 5 coefficients per state for the number of samples 2 people, 4 coefficients per state for the number of samples 3, 4 and 5 people, 3 coefficient per state for the number samples 6 and 7 people, and 5 coefficients per state for the number of samples 8, 9 and 10 people. And to get an accuracy of 100%, it takes 5 pieces of MFCC coefficients per state for the number of samples 2 to 4 people, 12 coefficients per state for the number of samples from 5 to 9. And for a number of samples 10 people, the highest accuracy of 99% with the number of MFCC coefficients of 12 per state. The experimental results also show that the MFCC feature extraction by using the number of coefficients per state as much as 5 coefficients has to give the average accuracy is better for all of the speakers compared using FFT which takes the number of samples 256 samples per state. [Yogyakarta] : Universitas Gadjah Mada 2011 Thesis NonPeerReviewed , Budi Darmawan and , Dr. Eng. Ir. Risanuri Hidayat, M.Sc. (2011) EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=51739 |
| spellingShingle | ETD , Budi Darmawan , Dr. Eng. Ir. Risanuri Hidayat, M.Sc. EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS |
| title | EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS |
| title_full | EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS |
| title_fullStr | EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS |
| title_full_unstemmed | EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS |
| title_short | EKSTRAKSI CIRI SUARA UNTUK PENGENALAN IDENTITAS PEMBICARA MENGGUNAKAN MFCC DAN HIDDEN MARKOV MODELS |
| title_sort | ekstraksi ciri suara untuk pengenalan identitas pembicara menggunakan mfcc dan hidden markov models |
| topic | ETD |
| work_keys_str_mv | AT budidarmawan ekstraksicirisuarauntukpengenalanidentitaspembicaramenggunakanmfccdanhiddenmarkovmodels AT drengirrisanurihidayatmsc ekstraksicirisuarauntukpengenalanidentitaspembicaramenggunakanmfccdanhiddenmarkovmodels |