What is Automatic Speech Recognition (ASR)?
Automatic Speech Recognition (ASR) is a technology that converts spoken language into written text in real time or from recorded audio. It’s the core technology behind voice assistants, dictation software, voice commands, and transcription services.
ASR enables machines to “understand” human speech by processing acoustic signals and turning them into linguistically meaningful text, making human-computer interaction more natural and hands-free.
How Does ASR Work?
ASR systems use a combination of signal processing, machine learning, and natural language processing techniques to convert speech into text. The workflow can be broken into the following steps:
1. Audio Input / Signal Processing
- Captures spoken audio via microphone or uploaded file.
- Converts analog audio signals into digital format (sampling and digitization).
- Removes noise and enhances speech using filters.
2. Feature Extraction
- Extracts useful audio features like MFCC (Mel-frequency cepstral coefficients), spectrograms, or filter banks.
- These features represent the acoustic properties of speech and help distinguish sounds (phonemes).
3. Acoustic Modeling
- Maps short segments of audio to phonemes (basic sound units in a language).
- Traditionally used Hidden Markov Models (HMMs); now often replaced or enhanced by Deep Neural Networks (DNNs), LSTMs, or Transformers.
4. Language Modeling
- Predicts the sequence of words that are most likely given the acoustic input.
- Uses statistical or deep learning models to handle grammar, word combinations, and context.
- Pretrained language models (like BERT, GPT) are increasingly used in modern ASR systems.
5. Decoding
- Combines the acoustic model and language model to output the most likely transcription.
- Performs beam search or other algorithms to identify the best word sequence.
6. Post-processing
- Adds punctuation, capitalization, and formatting.
- Corrects transcription errors using contextual or grammar rules.
- May include speaker diarization (who said what), and time-stamping for transcripts.