• Jyoti Gurav
• Nandini Patil
• Prajakta Vibhute
• Sherya Gangdhar
• Anuradha Shid

Speech-to-text (STT) conversion, also known as automatic speech recognition (ASR), refers to the process of converting spoken language into written text. This technology has gained significant attention in recent years due to its potential applications across various domains, including healthcare, customer service, education, and accessibility. The primary goal of STT systems is to accurately transcribe human speech in real-time or from recorded audio data while handling variations in accents, speech patterns, background noise, and other real-world challenges. The STT conversion process typically involves several stages, including audio signal preprocessing, feature extraction, pattern recognition, and text generation. Initially, the input audio is captured using microphones or recording devices, followed by the extraction of relevant features such as Mel-frequency cepstral coefficients (MFCCs) to represent the speech signal. These features are then processed using machine learning algorithms—ranging from traditional hidden Markov models (HMMs) to more advanced deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), including Long Short-Term Memory (LSTM) networks. These models are trained on large datasets to recognize and predict phonetic, linguistic, and contextual elements of speech. Key challenges faced by speech-to-text systems include handling homophones, speech disfluencies, speaker variability, noise interference, and real-time processing demands. To improve accuracy and robustness, STT systems often incorporate techniques such as noise filtering, speaker adaptation, language modeling, and context-based corrections.

"Speech to Text Conversion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.c77-c84, March-2025, Available :http://www.jetir.org/papers/JETIR2503210.pdf

"Speech to Text Conversion", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppc77-c84, March-2025, Available at : http://www.jetir.org/papers/JETIR2503210.pdf