Speech recognition a subset of natural language processing, converts spoken language into text format. This technology employs acoustic and language models, often utilizing deep learning techniques like Recurrent Neural Networks (RNNs) and Transformers. The process involves audio signal preprocessing, feature extraction, and model training on diverse speech data. Applications span from virtual assistants like Siri, transcription services, to accessibility tools. Advancements like neural architectures have enhanced accuracy across languages and accents.

1. Audio Input: Capturing spoken language through devices like microphones or audio recordings.

2. Preprocessing: Cleaning and enhancing the audio signal by removing noise, normalizing volume, and potentially segmenting longer recordings into manageable chunks.

3. Feature Extraction: Transforming the audio waveform into a suitable representation, often using techniques like Mel Frequency Cepstral Coefficients (MFCCs) to capture relevant speech features.

4. Acoustic Modeling: Building a model that learns to associate acoustic features with phonemes or sub-word units, utilizing techniques such as Hidden Markov Models (HMMs) or deep learning methods like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

5. Language Modeling: Creating a language model that predicts the likelihood of word sequences, improving recognition accuracy by considering contextual information.

6. Alignment: Matching the acoustic features with the corresponding linguistic units to form words and sentences, ensuring accurate transcription.

7. Decoding: Applying statistical techniques or neural networks to determine the most probable word sequence given the acoustic and language models.

8. Post-processing: Refining the output, which may involve spell-checking, grammar correction, and handling homophones.