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Language modeling for conversational speech using neural network model is a challenging task due to unconstrained speaking style, frequent grammatical errors, hesitations, start-overs and other variability associated with audio signal transcriptions. All these made speech language modeling inadequate because collecting large amount of frame-based training data with detailed description remains very costly. This paper therefore, proposes a new method of language modeling to capture the acoustic knowledge required in neural networks to map speech text to speakers. The speaker identification system (SIS), implemented with C++, is equipped with data abstraction, to adapt many speakers under various conditions to achieve speaker independency. This increases robustness and reduces out-of-vocabulary (OOV) rate significantly. An accelerated training algorithm was implemented to recognize spoken words to achieve efficient supervised learning than using frame-based speech data. Developed system is applicable where non-audio communication is desired.