What is Automatic Speech Recognition (ASR)

How It Works

Modern ASR systems use end-to-end neural models that directly map audio waveforms or spectrograms to text sequences. The audio is encoded into feature representations, and a decoder generates the text transcript. Attention mechanisms align audio frames with text tokens. Language model integration helps resolve ambiguities and improve fluency of the output transcript.

Technical Details

Leading models include Whisper (OpenAI, multilingual), Conformer (Google), and wav2vec 2.0 (Meta). Whisper supports 99 languages and includes built-in timestamp prediction. Output can include word-level timestamps, confidence scores, and language detection. Performance is measured using Word Error Rate (WER). Models range from tiny (39M parameters, real-time on CPU) to large (1.5B parameters, requiring GPU).

Best Practices

• Use Whisper for multilingual and general-purpose transcription out of the box
• Choose model size based on accuracy needs and compute constraints
• Apply VAD preprocessing to skip non-speech segments and reduce processing time
• Post-process transcripts with punctuation restoration and formatting for readability

Common Pitfalls

• Not testing on domain-specific audio that may contain technical jargon or accents
• Using a model too large for the available compute, causing unacceptable latency
• Trusting timestamps blindly without validating alignment accuracy
• Not handling background noise, overlapping speech, or low-quality audio recordings

Advanced Tips

• Fine-tune Whisper on domain-specific data for improved accuracy on specialized vocabulary
• Combine ASR with speaker diarization for speaker-attributed transcripts
• Use ASR transcripts as searchable text metadata for audio and video content indexing
• Implement streaming ASR for real-time transcription in live multimodal applications