Speech Recognition

Speech recognition is AI that converts spoken words into written text. When you dictate a message on your phone, ask Alexa a question, or use automated transcription for a meeting — speech recognition is doing the work.

How modern speech recognition works

Early speech recognition systems used hand-crafted rules about how sounds map to words. Modern systems use deep learning:

1. Audio processing: The raw audio waveform is converted into a spectrogram — a visual representation of sound frequencies over time
2. Feature extraction: A neural network analyses the spectrogram to identify acoustic features
3. Language modelling: The system uses context and language patterns to resolve ambiguities (did you say "recognise speech" or "wreck a nice beach"?)
4. Output: The final text is produced, often with punctuation and formatting applied automatically

Key technologies

• Whisper (OpenAI): An open-source model that handles multiple languages and is robust to background noise
• Google Speech-to-Text: Cloud API with real-time streaming recognition
• Amazon Transcribe: AWS service optimised for business applications
• Apple/Google on-device: Smaller models that run locally on phones for privacy

Challenges

Speech recognition has improved dramatically but still struggles with:

• Accents and dialects: Models trained primarily on standard accents may perform poorly on regional speech
• Background noise: Noisy environments degrade accuracy significantly
• Technical vocabulary: Domain-specific jargon (medical, legal, engineering) requires fine-tuning
• Multiple speakers: Identifying who said what (diarisation) remains an active research area
• Homophones: Distinguishing "their," "there," and "they're" requires language understanding

Business applications

• Meeting transcription: Automatic transcription and summarisation of meetings (Otter, Fireflies)
• Call centre analytics: Transcribing and analysing customer calls at scale
• Accessibility: Making content accessible to deaf and hard-of-hearing users
• Voice interfaces: Voice-controlled applications, dictation, and hands-free operation
• Healthcare: Clinical documentation from doctor-patient conversations

The integration with LLMs

Speech recognition becomes especially powerful when combined with large language models. Transcribe a meeting, then use an LLM to summarise it, extract action items, and draft follow-up emails. This pipeline — speech to text to intelligence — is one of the most practical AI workflows for professionals.