Development of a MFCC-SVM Based Turkish Speech Recognition system

Tombaloğlu, Burak; Erdem, Hamıt

doi:10.1109/siu.2016.7495893

Cited by 6 publications

(5 citation statements)

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“…Tests conducted using soft margin (SM-SVM) and least square (LS-SVM) methods yielded success rates of 91% and 71%, respectively. In another study conducted in 2016, a phoneme-based practice was performed with a performance rate of 84% [66]. In addition, in a study in which Bayesian networks and J48 classifiers [67] were compared with the SVM method, it was demonstrated that the best results for the detection of emotions were achieved with the SVM and classification was performed with 80% accuracy.…”

Section: Support Vector Machines (Svms)mentioning

confidence: 99%

A detailed survey of Turkish automatic speech recognition

2020

Turk J Elec Eng & Comp Sci

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Significant improvements have been made in automatic speech recognition (ASR) systems in terms of both the general technology and the software used. Despite these advancements, however, there is still an important difference between the recognition performance of humans and machines. This work focuses on the studies conducted in the field of Turkish speech recognition, the progress made in such studies in recent years, the language-specific constraints, the performance results achieved in the applications developed to date, and the development of a general scheme for researchers wishing to develop an ASR system for the Turkish language. A comprehensive study on the Turkish language, including all literature and speech recognition systems, was prepared. There has been significant progress in the languages of developed countries in terms of ASR systems, but it was observed in this work that the opposite is true for the Turkish language. The lack of labor force and material resources remains the biggest obstacle to developing better systems. As a result, concrete studies are needed to achieve an ASR system with performance equivalent to the human level for the Turkish language.

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Section: Support Vector Machines (Svms)mentioning

confidence: 99%

A detailed survey of Turkish automatic speech recognition

2020

Turk J Elec Eng & Comp Sci

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“…Bu pencereler daha sonra Mel filtre bankasını oluşturmak için Ayrık Fourier Dönüşümü (DFT: Discrete Fourier Transform) ile birlikte kullanılmaktadır. Mel frekans eğrisine göre filtrelerin genişliği değişmekte ve böylece merkez frekansı etrafındaki kritik bant üzerindeki özellikler hesaplanmaktadır [33]. Kaldi, en çok kullanılan özellik çıkarım teknikleri için standart özellikleri (Cepstral sayıları, en düşük ve en yüksek frekans kesmeleri vb.)…”

Section: B 1 öZellik çıKarımıunclassified

“…Veriler akustik veriler ve dil verileri olarak iki bölüme ayrılmıştır. Kaldi'nin gereksinim duyduğu akustik veriler için zorunlu olan meta verilerinin hazırlanması adımları verilmiştir [33]:…”

Section: B öN İşlem Modülünün Geli̇şti̇ri̇lmesi̇unclassified

The Effect of Removal the Silence and Speech Parsing Processes on Turkish Automatic Speech Recognition

Oyucu

Polat

Sever

2020

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Otomatik Konuşma Tanıma sistemleri temel olarak akustik bilgiden faydalanılarak geliştirilmektedir. Akustik bilgiden fonem bilgisinin elde edilmesi için eşleştirilmiş konuşma ve metin verileri kullanılmaktadır. Bu veriler ile eğitilen akustik modeller gerçek hayattaki bütün akustik bilgiyi modelleyememektedir. Bu nedenle belirli ön işlemlerin yapılması ve otomatik konuşma tanıma sistemlerinin başarımını düşürecek akustik bilgilerin ortadan kaldırılması gerekmektedir. Bu çalışmada konuşma içerisinde geçen sessizliklerin kaldırılması için bir yöntem önerilmiştir. Önerilen yöntemin amacı sessizlik bilgisinin ortadan kaldırılması ve akustik bilgide uzun bağımlılıklar sağlayan konuşmaların parçalara ayrılmasıdır. Geliştirilen yöntemin sonunda elde edilen sessizlik içermeyen ve parçalara ayrılan konuşma bilgisi bir Türkçe Otomatik Konuşma Tanıma sistemine girdi olarak verilmiştir. Otomatik Konuşma Tanıma sisteminin çıkışında sisteme giriş olarak verilen konuşma parçalarına karşılık gelen metinler birleştirilerek sunulmuştur. Gerçekleştirilen deneylerde sessizliğin kaldırılması ve konuşmanın parçalara ayrılması işleminin Otomatik Konuşma Tanıma sistemlerinin başarımını artırdığı görülmüştür.

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“…Yapılan araştırmalar sonucu Markov sürecindeki geçiş oranlarının Türkçe konuşma tanıma sistemlerinin performansını ve davranışını belirlemek için kolaylıkla kullanılabileceği sonucuna varılmıştır. Ani ortam değişikliklerinin meydana getireceği güç değişkenlikleri, ağ kontrol sistemleri ve üretim sistemlerinde bulunan bazı pratik yöntemleri modellemek için en uygun yaklaşımlar Markov modellerinde mevcuttur [92]. Markov zincirleri, gürültü modellemesi ve konuşma sinyalinin tahmini için etkili bir şekilde uygulanmaktadır.…”

Section: Türkçe Asr'nin Geleceği (The Future Of Turkish Asr)unclassified

Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu

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Sever

Polat

2019

Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım Ve Teknoloji

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Figure A shows the application area of the Applications Automatic Speech Recognition (ASR) system. Adaptation is required for adaptation processes to be carried out according to the Application area. During the Speech Processing phase, which is the first entry point of the ASR system, feature extraction is performed from the audio signal. Individual properties are obtained by different feature extraction techniques. For example, Mel Frequency Cepstral Coefficient (MFCC) is a feature extraction technique commonly used in speech recognition systems. Decoder, one of the other components of ASR, converts the feature vectors obtained by using Acoustic Model (AM) and Language Model (LM) into phoneme sequences. In acoustic modeling, firstly, the posterior probability of the phoneme within a given time signal is calculated. In the artificial neural network-based acoustic model, the posterior probability of phonemes is independent for each window. This independence means that the phonemes in a word are independent of each other. Figure A. Basic architecture of speech recognition system.Purpose: This study presents a literature review on speech recognition and then discusses the recorded signs of progress made in this area of research for different languages. The data sets used in speech recognition systems, feature extraction approaches, speech recognition methods and performance evaluation criteria are examined and the focus is on the development of speech recognition and the difficulties in this field. Theory and Methods:In this study, literature review (systematic), which is an important component for a scientific article, was carried out. This process was carried out by the combination of different methods. A combination of review approaches is given.Results: According to the information obtained as a result of the research; Computational architectures that can be applied to resistance to the acoustic environment, self-learning in ASR, detection of unknown words, the success of the Turkish ASR at a broad and limited repertoire level, insufficient source status and Automatic Speech Recognition ASR were evaluated. In addition, the future of Turkish ASR was discussed and recommendations were made to overcome the current difficulties for Turkish ASR. Conclusion:The aim of this study is to examine the current speech recognition methods and approaches and to present the developments in this field in detail. For this reason, approaches, datasets and the difficulties faced by the researchers in their studies in this field are discussed in the scope of the study. The effect of deep learning and classical approaches on ASR was investigated. A road map is provided for researchers to incorporate the detailed information necessary for their work in this field to their own work and to overcome the present challenges.

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Development of a MFCC-SVM Based Turkish Speech Recognition system

Cited by 6 publications

References 0 publications

A detailed survey of Turkish automatic speech recognition

A detailed survey of Turkish automatic speech recognition

The Effect of Removal the Silence and Speech Parsing Processes on Turkish Automatic Speech Recognition

Otomatik Konuşma Tanımaya Genel Bakış, Yaklaşımlar ve Zorluklar: Türkçe Konuşma Tanımanın Gelecekteki Yolu

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