AiVibeAutomatic Speech Recognition (ASR)
Automatic speech recognition (ASR), or speech-to-text, converts spoken language into written text using neural acoustic and language modelling, enabling voice commands, transcription, captioning and voice-driven assistants.
Older systems combined acoustic models, pronunciation dictionaries and statistical language models. Modern ASR uses end-to-end neural networks, such as transformer and conformer architectures trained with connectionist temporal classification or encoder-decoder objectives, mapping audio features directly to text. OpenAI's Whisper is a widely used open model trained on a large multilingual audio dataset. Streaming ASR produces text with low latency while people speak.
Uses include voice interfaces for workers whose hands are busy, meeting and call transcription, captioning, voice assistants, dictation of inspection notes and spoken queries to machine data. Combined with language models and text-to-speech, ASR enables natural spoken conversations with software.
Accuracy falls with background noise, accents, domain vocabulary and overlapping speakers, all of which are common on shop floors. Noise-cancelling microphones, custom vocabularies and fine-tuning help. Accuracy is measured by word error rate. Recording speech raises consent and privacy requirements, and on-device or edge ASR keeps audio local.
Key points
- Converts spoken language into text
- Modern systems use end-to-end neural networks
- Accuracy is measured by word error rate
- Noise, accents and jargon reduce accuracy on shop floors
Where AiVibe comes in
AiVibe delivers AI and machine learning services, chatbots and virtual assistants with RAG, MCP tools and voice, AI quality management including bias detection and model validation, and the AIMURUGA AI agent, and builds Intel-based edge AI devices using the Intel Distribution of OpenVINO toolkit.