scholarly journals IMPLEMENTASI METODE PATTERN RECOGNITION UNTUK PENGENALAN UCAPAN HURUF HIJAIYYAH

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Ersa Triansyah ◽  
Youllia Indrawaty N
Keyword(s):  
Pattern Recognition ◽  
Dynamic Time Warping ◽  
Threshold Value ◽  
Sound Signal ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Voice Signal ◽  
Signal Features ◽  
Cepstral Coefficients ◽  
Dynamic Time

[Id] Pattern recognition memiliki kemampuan untuk mengenali suara dengan melakukan pengenalan pola suara melalui fitur-fitur sinyal suara yang kemudian dilakukan pengenalan pola melalui perbandingan pola suara uji dengan suara referensi. Untuk mendapatkan fitur-fitur sinyal suara, diperlukan metode untuk mengekstraksi sinyal suara sehingga fitur-fitur sinyal suara yang dibutuhkan terpenuhi. MFCC (Mel Frequency Cepstral Coefficients) merupakan alternatif metode untuk melakukan ektraksi sinyal yang menghasilkan koefisien cepstral dari sinyal suara. Koefisien cepstral sinyal suara dari hasil ektraksi tersebut, kemudian dilakukan perbandingan kesesuaian antara suara uji dan suara referensi. DTW (Dynamic Time Warping) salah satu algoritma untuk dapat melakukan perbandingan koefisien tersebut. Dalam kasus pegenalan ucapan huruf hijaiyyah umumnya dilakukan secara talaqqi (belajar intensif) antar seorang guru dengan murid, penilaian yang dilakukan bersifat subjektif berdasarkan kemampuan indera dari seorang guru, untuk itu aplikasi pengucapan huruf hijaiyyah merupakan salah satu alternatif untuk mengenali dan menguji kesesuaian ucapan secara objektif melalui penghitungan matematis dengan melakukan pengenalan pola suara. Dari pengujian yang telah dilakukan, dari 6 orang yang diuji melakukan pengucapan 29 huruf 3 tanda baca dan pengulang sebanyak 5 kali menghasilkan persentase kecocokan suara mencapai di atas 90 %, nilai threshold 1,3 Kata kunci: Speech Recognition, Pattern Recognition, MFCC, DTW, Hijaiyyah [En] Pattern recognition has ability to recognize voice by voice pattern recognition through voice signal features which then carried out voice pattern recognition through comparison of tester voice pattern with a reference voice. To get the sound signal features, it needs a method for extracting sound signal so that required sound signals features are fulfilled. MFCC is an alternative method to perform signal extraction which is produce cepstral coefficients of the sound signal. Cepstral coefficients of sound signal from the extraction then will be compared by the match between tester voice and reference voice. DTW is one of algorithm to do a comparison of the coefficients. In the case of introducing hijaiyyah generally talaqqi (intensively) conducted between a teacher and students, the appraisal is subjective based on the sensory capabilities of the teacher, therefore hijaiyyah pronunciation application is an alternative to identify and test the suitability of speech objectively through mathematical calculations by performing voice pattern recognition. From the testing that has been done, from 6 people tested do pronunciations 29 letters and punctuation repeater 3 to 5 times the yield percentage matches the sound reaches above 90%, a threshold value of 1.3.

scholarly journals IMPLEMENTASI TEKNIK DYNAMIC TIME WARPING (DTW) PADA APLIKASI SPEECH TO TEXT

2018 ◽  
Vol 10 (1) ◽  
pp. 49-58
Author(s):  
Candra Dinata ◽  
Diyah Puspitaningrum ◽  
Ernawati Erna
Keyword(s):  
Dynamic Time Warping ◽  
Test Word ◽  
Simulation Software ◽  
Sound Signal ◽  
Human Beings ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Sound Processing ◽  
Cepstral Coefficients ◽  
Dynamic Time

ABSTRAK Suara/ucapan adalah salah satu cara kita sebagai manusia untuk berkomunikasi dan mengekspresikan diri. Speech to text (ucapan ke text), merupakan salah satu bidang sains computer yaitu bidang pengolahan suara. Speech to text (STT) adalah penerjemahan kalimat (kata yang diucapkan) ke dalam text. STT merupakan proses pengolahan suatu sinyal suara, mengekstrak fitur dari sinyal suara tersebut yang selanjutkan dibandingkan dengan hasil ekstraksi dari sinyal suara yang lain untuk dapat dikenali persamaannya. Penelitian ini merancang dan membangun suatu program aplikasi Speech to Text yang mampu identifikasi suatu sinyal suara menggunakan perangkat lunak simulasi MATLAB R2016a. Terdapat dua proses umum pada bidang pengolahan suara, yaitu ekstraksi fitur dan pencocokan fitur. Pada sistem ini metode mel-frequency cepstral coefficients digunakan untuk mengekstraksi fitur dan metode dynamic time warping digunakan untuk pencocokan fitur. Metode DTW yang digunakan dapat menghitung jarak atau selisih antara dua data yang dibandingkan. Rata-rata akurasi yang didapat setelah dilakukan percobaan pada pengujian kata adalah 95.85% dan pada pengujian kalimat adalah 94%.   ABSTRACT Voice / speech is one of the ways we as human beings to communicate and express themselves. Speech to text (STT), is one of computer science is the field of sound processing. Speech to text (STT) is the translation of the sentence (the spoken word) in the text. STT is a voice signal processing, extracting features from the speech signal and then compared it with the extraction of the other sound signal to recognize the signal similarities. This research design and build an application program Speech to Text that is capable of identifying a sound signal using simulation software MATLAB R2016a. There are two common processes in the field of sound processing, feature extraction and matching features. In this system, the method mel-frequency cepstral coefficients are used to extract features and dynamic time warping method used for matching features. DTW method used can calculate the distance or the difference between the two data being compared. The average accuracy is obtained after experiments on the test word was 95.85% and the testing of the sentence is 94%. How to Cite : Dinata, C. Puspitaningrum, D. Erna, E. (2017). IMPLEMENTASI TEKNIK DYNAMIC TIME WARPING (DTW) PADA APLIKASI SPEECH TO TEXT. Jurnal Teknik Informatika, 10(1), 49-58. doi:10.15408/jti.v10i1.6816 Permalink/DOI: http://dx.doi.org/10.15408/jti.v10i1.6816

Inversion of speech by non-linear transformation of temporary

2016 ◽  
Vol 1 (1) ◽  
pp. 139-150
Author(s):  
Robert Wielgat ◽  
Anita Lorenc
Keyword(s):  
Dynamic Time Warping ◽  
Mean Square ◽  
Time Warping ◽  
Mel Frequency Cepstral Coefficients ◽  
Precise Method ◽  
Electromagnetic Articulography ◽  
Acoustic Speech Signal ◽  
Preliminary Research ◽  
Dynamic Time ◽  
Mean Square Errors

Electromagnetic Articulography (EMA) is a precise method for speech articulators assessment which is carried out by sensors placed mainly on the tongue. Various methods are being developed in order to avoid the assessment by EMA sensors. One of them is speech inversion. Here preliminary research on speech inversion based on dynamic time warping (DTW) method has been described. Mel-frequency cepstral coefficients (MFCC) method has been chosen as the acoustic speech signal parametrization method. Root mean square errors (RMSE) of the evaluation have been presented and discussed.

scholarly journals DTW–RADON-BASED SHAPE DESCRIPTOR FOR PATTERN RECOGNITION

2013 ◽  
Vol 27 (03) ◽  
pp. 1350008 ◽  
Author(s):  
K. C. SANTOSH ◽  
BART LAMIROY ◽  
LAURENT WENDLING
Keyword(s):  
Pattern Recognition ◽  
Dynamic Time Warping ◽  
Recognition Performance ◽  
Shape Descriptor ◽  
Shape Descriptors ◽  
Time Warping ◽  
Recognition Method ◽  
Dynamic Time ◽  
Public Datasets ◽  
Comprehensive Study

In this paper, we present a pattern recognition method that uses dynamic programming for the alignment of Radon features. The key characteristic of the method is to use dynamic time warping (DTW) to match corresponding pairs of the Radon features for all possible projections. Thanks to DTW, we avoid compressing the feature matrix into a single vector which would otherwise miss information. To reduce the possible number of matchings, we rely on a initial normalization based on the pattern orientation. A comprehensive study is made using major state-of-the-art shape descriptors over several public datasets of shapes such as graphical symbols (both printed and hand-drawn), handwritten characters and footwear prints. In all tests, the method proves its generic behavior by providing better recognition performance. Overall, we validate that our method is robust to deformed shape due to distortion, degradation and occlusion.

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