Deep Learning Based Automatic Speech Recognition for Turkish
Using Deep Neural Networks (DNN) as an advanced Artificial Neural Networks (ANN) has become widespread with the development of computer technology. Although DNN has been applied for solving Automatic Speech Recognition (ASR) problem in some languages, DNN-based Turkish Speech Recognition has not bee...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sakarya University
2020-08-01
|
| Series: | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| Subjects: | |
| Online Access: | https://dergipark.org.tr/tr/download/article-file/1210062 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850252236363399168 |
|---|---|
| author | Hamit Erdem Burak Tombaloğlu |
| author_facet | Hamit Erdem Burak Tombaloğlu |
| author_sort | Hamit Erdem |
| collection | DOAJ |
| description | Using Deep Neural Networks (DNN) as an advanced Artificial Neural Networks (ANN) has become widespread with the development of computer technology. Although DNN has been applied for solving Automatic Speech Recognition (ASR) problem in some languages, DNN-based Turkish Speech Recognition has not been studied extensively. Turkish language is an agglutinative and a phoneme-based language. In this study, a Deep Belief Network (DBN) based Turkish phoneme and speech recognizer is developed. The proposed system recognizes words in the system vocabulary and phoneme components of out of vocabulary (OOV) words. Sub-word (morpheme) based language modelling is implemented into the system. Each phoneme of Turkish language is also modelled as a sub-word in the model. Sub-word (morpheme) based language model is widely used for agglutinative languages to prevent excessive vocabulary size. The performance of the suggested DBN based ASR system is compared with the conventional recognition method, GMM (Gaussian Mixture Method) based Hidden Markov Model (HMM). Regarding to performance metrics, the recognition rate of Turkish language is improved in compare with previous studies. |
| format | Article |
| id | doaj-art-9d062f13260243f79d76ea14c49d8239 |
| institution | OA Journals |
| issn | 2147-835X |
| language | English |
| publishDate | 2020-08-01 |
| publisher | Sakarya University |
| record_format | Article |
| series | Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi |
| spelling | doaj-art-9d062f13260243f79d76ea14c49d82392025-08-20T01:57:43ZengSakarya UniversitySakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi2147-835X2020-08-0124472573910.16984/saufenbilder.71188828Deep Learning Based Automatic Speech Recognition for TurkishHamit Erdem0https://orcid.org/0000-0003-1704-1581Burak Tombaloğlu1https://orcid.org/0000-0003-3994-0422BAŞKENT ÜNİVERSİTESİBAŞKENT ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜUsing Deep Neural Networks (DNN) as an advanced Artificial Neural Networks (ANN) has become widespread with the development of computer technology. Although DNN has been applied for solving Automatic Speech Recognition (ASR) problem in some languages, DNN-based Turkish Speech Recognition has not been studied extensively. Turkish language is an agglutinative and a phoneme-based language. In this study, a Deep Belief Network (DBN) based Turkish phoneme and speech recognizer is developed. The proposed system recognizes words in the system vocabulary and phoneme components of out of vocabulary (OOV) words. Sub-word (morpheme) based language modelling is implemented into the system. Each phoneme of Turkish language is also modelled as a sub-word in the model. Sub-word (morpheme) based language model is widely used for agglutinative languages to prevent excessive vocabulary size. The performance of the suggested DBN based ASR system is compared with the conventional recognition method, GMM (Gaussian Mixture Method) based Hidden Markov Model (HMM). Regarding to performance metrics, the recognition rate of Turkish language is improved in compare with previous studies.https://dergipark.org.tr/tr/download/article-file/1210062deepneuralnetworksbeliefautomaticspeechrecognitionturkishdeep belief networks |
| spellingShingle | Hamit Erdem Burak Tombaloğlu Deep Learning Based Automatic Speech Recognition for Turkish Sakarya Üniversitesi Fen Bilimleri Enstitüsü Dergisi deep neural networks belief automatic speech recognition turkish deep belief networks |
| title | Deep Learning Based Automatic Speech Recognition for Turkish |
| title_full | Deep Learning Based Automatic Speech Recognition for Turkish |
| title_fullStr | Deep Learning Based Automatic Speech Recognition for Turkish |
| title_full_unstemmed | Deep Learning Based Automatic Speech Recognition for Turkish |
| title_short | Deep Learning Based Automatic Speech Recognition for Turkish |
| title_sort | deep learning based automatic speech recognition for turkish |
| topic | deep neural networks belief automatic speech recognition turkish deep belief networks |
| url | https://dergipark.org.tr/tr/download/article-file/1210062 |
| work_keys_str_mv | AT hamiterdem deeplearningbasedautomaticspeechrecognitionforturkish AT buraktombaloglu deeplearningbasedautomaticspeechrecognitionforturkish |