Decoding Soundscapes: Machine Learning Approaches for Speech-to-Text Conversion

Abstract: The conversion of audio signals into text has become essential in human–computer interaction, accessibility, and information management. With the rise of multimedia and real-time communication, machine learning (ML) has revolutionized speech recognition by bridging spoken and written language more effectively than traditional rule-based systems. Modern ML models use supervised and unsupervised learning to process acoustic features such as MFCCs, spectrograms, and deep audio embeddings through architectures like RNNs, CNNs, and transformers, enabling accurate and context-aware transcription. End-to-end deep learning approaches, including sequence-to-sequence and attention mechanisms, have further enhanced performance by minimizing manual feature design. Advances in transfer learning, multilingual pre-training, and self-supervised learning have expanded speech recognition to low-resource languages, as demonstrated by large models like wav2vec and Whisper. These innovations have broad applications in voice assistants, captioning, healthcare documentation, legal and industrial transcription, and accessibility tools, delivering significant social and economic benefits. However, challenges such as noise, code-switching, domain adaptation, high computational demands, and ethical concerns related to privacy, bias, and fairness persist. Ongoing research focuses on improving the robustness, efficiency, and inclusivity of ML-based speech recognition systems for universal and responsible deployment.