NATURAL LANGUAGE PROCESSING AND SPEECH RECOGNITION

Paper ID: EIJTEM_2023_10_1_32-38

Author's Name: Manoj Pal

Volume: 10

Issue: 1

Year: 2023

Page No: 32-38

Abstract:

Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) have experienced unprecedented progress in 2023, driven by the rapid advancement of transformer-based architectures, large-scale pretrained models, and multilingual datasets. This paper investigates the state-of-the-art developments in both fields, with a focus on the convergence of language and speech technologies. We explore the architecture and performance of leading models such as OpenAI’s Whisper, Meta’s wav2vec 2.0, and Google’s Universal Speech Model, analyzing their impact on speech-to-text accuracy, multilingual processing, and real-time transcription. A comparative evaluation is conducted using public datasets including LibriSpeech and Common Voice, applying standard metrics such as Word Error Rate (WER) and BLEU scores. Our findings demonstrate significant improvements in low-resource language recognition, contextual understanding, and noise robustness. Furthermore, we examine the integration of ASR and NLP in conversational AI, accessibility tools, and customer service automation. The results indicate a clear trend toward unified, multimodal AI systems capable of seamless human-machine interaction. This study contributes to the understanding of the technological landscape in 2023 and outlines key areas for future research, such as ethical considerations, edge deployment, and zero-shot learning in speech and language processing.

Keywords: Natural Language Processing (NLP); Automatic Speech Recognition (ASR); Deep Learning; Transformers; Multilingual Models; Speech-to-Text

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