Mel-Frekans Kepstral Katsayılar ve Gizli Markov Model Kullanılarak Türkçe Konuşma Tanıma

Öz

Bu makalede, Türkçe söylenen sayıların tanınmasına yönelik yeni bir sistem önerilmiştir. Özellik çıkarımı yöntemi olarak Mel frekanslı Kepstral Katsayıları (MFKK) algoritması, her fonetik modelleme olarak ise Gaussian Gizli Markov modeli kullanılmıştır. 7 kadın ve 13 erkekten oluşan 20 denekten toplanan eğitim veri setinde Türkçe rakamların 0'dan 10'a kadar olduğunu söyleyen ses dosyaları vardır. Her dosyada yalıtılmış bir ortamda kaydedilen saniyede 8000 Hz'de örneklenen ve 1 saniye uzunluğunda ses bulunmaktadır. Sistem, farklı kişilerden alınan rastgele kayıtlar kullanarak test edilmiştir. Eğitim dosyaları 220, test dosyaları ise 18 ses içermektedir. Sistem testlerde % 83.3 doğruluk,% 86 hassasiyet ve% 83 hatırlama oranlarına ulaşmıştır.

Anahtar Kelimeler

• Gizli Markov Modeli,Mel-Frekans Kepstral Katsayılar,Türkçe Konuşma Tanıma

Turkish Speech recognition using Mel-frequency cepstral coefficients(MFCC) and Hidden Markov Model (HMM)

Öz

In this paper, a new Turkish spoken number recognition system proposed. The Mel-frequency cepstral coefficients (MFCC) algorithm used as a feature extraction method, the Gaussian Hidden Markov model, used for numbers phonemes modeling where each number has a Markov model. The system trained on a dataset collected from 20 subjects that includes 7 females and 13 males. Each one says the Turkish numbers from “zero” to “ten”. Audio files sampled at 8000Hz at each second and each file has one-second length and recorded in an isolated environment. We tested the system using random records for different people. The training files include 220 audio record and testing files include 18 audio record. The system achieves %83.3 accuracy, %86 precision, and %83 recall rates.

Anahtar Kelimeler

• Hidden Markov Model,Mel-Frequency Cepstral Coefficients,Turkish Speech Recognition

Kaynakça

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