Türkçe Günlük Kelime ve İfadeler Kullanarak CNN ve LSTM ile Görsel Konuşma Tanıma

Year 2024, Volume: 8 Issue: 2, 69 - 75

Ali Berkol , Nergis Pervan Akman , Talya Tümer Sivri , Hamit Erdem

Abstract

Dudak okuma; el hareketleri, jestler ve yüz ifadeleri gibi konuşma örüntülerini, hareketlerini ve mimiklerini değerlendirmek amacıyla bir konuşmacının yüzünü incelemek olarak tanımlanmaktadır. Bilgisayarlara dudak okuma yeteneği kazandırma çalışmaları, derin öğrenmede sınıflandırma ve örüntü tanıma alanında büyüyen bir araştırma alanıdır ve günümüzde hâlâ çözülmesi gereken açık problemler barındırmaktadır. Son yıllarda, farklı dillerde konuşmayı metne dönüştürmek ve sınıflandırmak için çeşitli yöntemler geliştirilmiş ve uygulanmıştır. Ayrıca, çoğu yöntemde çok modlu veriler, yani konuşma ve görüntü verileri birleştirilmiştir. Bu çalışma, görüntülerle yeni Türkçe dudak okuma verileri sağlamayı ve Türkçe günlük kelimeler için yüksek doğrulukta bir sınıflandırma yöntemi sunmayı amaçlamaktadır. Kullanılan veriler, YouTube platformundan toplanmıştır. Bu zorlu verilerle, günlük kelimeleri ve ifadeleri sınıflandırmak için Evrişimli Sinir Ağı (Convolutional Neural Network – CNN) ve Uzun Kısa-Süreli Bellek (Long Short-Term Memory – LSTM) eğitilmiştir. Birçok deney sonucuna göre, CNN modeli daha iyi performans göstermiştir. Çoklu model verileri kullanmadan yalnızca görüntüler kullanmak, belleğin yorgunluğunu önler ve hesaplama süresini azaltır. Ayrıca, literatürde sınırlı bir çeşitlilik olduğundan, bu çalışma çok sınıflı Türkçe bir veri seti sunmaktadır.

Keywords

dudak okuma, çoklu sınıf sınıflandırma, Türkçe veri seti, derin öğrenme

Supporting Institution

Aselsan-Bites

Visual Speech Recognition Using CNN and LSTM with Turkish Daily Words and Phrases

Abstract

Contemplating a speaker’s face to evaluate speech patterns, movements, gestures, and expressions can be described as lip reading. Gaining the ability to lip reading to computers is a growing research area and has open problems for classification and pattern recognition in deep learning. In the last years, various methods have been developed and applied in different languages to classify and convert speech to text. Moreover, most methods have combined multi-model data, i.e., speech and image. This study aims to provide new Turkish lip-reading data with only images and provide a high-accuracy classification method for Turkish daily words. Data was collected from the YouTube platform. Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models were trained to classify daily words and phrases with this challenging data. According to numerous experiment results, the CNN model worked better. Using only images, not multi-modal data, prevents the memory from fatigue and decreases the computation time. Furthermore, we provide a multiclass dataset in Turkish since there is a narrow variety in the literature.

Keywords

lip reading, multiclass classification, Turkish dataset, deep learning

Supporting Institution

Aselsan-Bites

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