A variety of handcrafted features as well as techniques for Speaker Recognition (SR), which combines Speaker Identification (SI) and Speaker Verification (SV), have been developed over the previous 5 decades. Automatic speaker recognition (ASR), also referred to as vocal biometric recognition, is one method of human biometric identification. ASR plays such a crucial part in so many applications, including voice assistants, transcription services, and many more, and it has attracted a lot of attention lately. This paper develops a hybrid deep learning framework for text-independent ASR system. This framework is based on the integration of Gated Recurrent Units in Recurrent Neural Networks (RNN-GRU) with Connectionist Temporal Classification (CTC) loss via the layers of a 2D Convolutional Neural Network (2D-CNN). Using the benchmark LJspeech dataset, the model has been evaluated using a performance metric, Error Rate (ER). The achieved ER rate of about 16–17% for 20 epochs underscores the promising progress in ASR technology, setting the stage for continued advancements in this field.