Comparative Analysis of Deep Recurrent Neural Networks for Speech Recognition

Recent years have seen tremendous advancements in speech recognition technology, which has become essential to many different applications, such as virtual assistants and transcription services. To improve the precision and resilience of speech recognition systems, this paper investigates the combined use of Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). This study proposes a model integration strategy that amalgamates the sequential learning capabilities inherent in RNNs with the spatial feature extraction capabilities of CNNs, leveraging their complementary strengths. A deep recurrent neural network (DRNN)-based sequence learning models have been employed to capture intricate sequential features. Within this study, multiple bidirectional variants of Long ShortTerm Memory (LSTM), Gated Recurrent Unit (GRU), and Simple RNN recurrent layers were incorporated into DRNN architectures, and their performances were evaluated comparatively. The findings indicated that integrating a convolutional layer with three bidirectional LSTM-based DRNN models yields superior performance.