# speech recognition

> automatic conversion of spoken language into text

**Wikidata**: [Q189436](https://www.wikidata.org/wiki/Q189436)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Speech_recognition)  
**Source**: https://4ort.xyz/entity/speech-recognition

## Summary
Speech recognition is the automatic conversion of spoken language into text. It is a subfield of natural language processing and computational linguistics that enables computers to understand and transcribe human speech. The technology has evolved from early systems like Audrey in 1952 to modern applications including voice assistants and transcription software.

## Key Facts
- Inception: 1952 with the development of Audrey, one of the first automatic speech recognizers
- Part of: Natural language processing and computational linguistics
- Related software: Dragon NaturallySpeaking, Julius, and Sensory's voice technology products
- Notable researcher: Raymond Kurzweil, American computer scientist and inventor born in 1948
- Academic classification: Academic discipline, subclass of computational linguistics and natural language processing
- Wikipedia languages: Available in 20+ languages including English, German, Spanish, French, and Chinese
- Industry applications: Used in artificial intelligence, biometrics, and voice technology manufacturing

## FAQs
### Q: What is speech recognition used for?
A: Speech recognition is used for converting spoken words into written text, enabling voice commands, transcription services, voice assistants like Siri and Alexa, dictation software, and accessibility tools for people with disabilities.

### Q: How does speech recognition work?
A: Speech recognition works by analyzing audio input, breaking it down into phonemes (basic sound units), matching these patterns against a language model, and converting them into text using statistical algorithms and machine learning models.

### Q: What are some examples of speech recognition software?
A: Examples include Dragon NaturallySpeaking (professional dictation software), Julius (open-source speech recognition engine), and Sensory's voice technology products used in various consumer devices.

## Why It Matters
Speech recognition technology has fundamentally transformed how humans interact with computers and digital devices. By enabling natural voice-based communication, it has made technology more accessible to people who cannot type or have limited mobility, expanded the capabilities of mobile devices, and created new paradigms for human-computer interaction. The technology powers virtual assistants that help millions of people daily with tasks ranging from setting reminders to controlling smart home devices. In professional settings, speech recognition has revolutionized fields like healthcare (medical transcription), legal services (dictation), and customer service (automated phone systems). As artificial intelligence continues to advance, speech recognition is becoming increasingly accurate and sophisticated, breaking down language barriers and making digital services available to broader populations worldwide. The technology represents a critical step toward more intuitive, natural interfaces that understand human communication in its most fundamental form - spoken language.

## Notable For
- Historical significance: One of the earliest applications of artificial intelligence, dating back to 1952 with Audrey
- Interdisciplinary nature: Bridges computer science, linguistics, and electrical engineering
- Commercial impact: Powers billion-dollar industries including virtual assistants, transcription services, and accessibility technology
- Continuous evolution: Has progressed from recognizing single digits to understanding natural conversational speech
- Global reach: Available in dozens of languages and used across virtually every industry and application

## Body
### Historical Development
Speech recognition technology began in 1952 with Audrey, developed at Bell Labs, which could recognize spoken digits. Throughout the 1960s and 1970s, research continued with systems like IBM's Shoebox and Carnegie Mellon's Harpy, which could recognize around 1,000 words. The 1980s saw the introduction of hidden Markov models (HMMs), which significantly improved accuracy. By the 1990s, commercial products like Dragon NaturallySpeaking emerged, offering consumer-grade speech recognition.

### Technical Foundations
Modern speech recognition systems use deep learning neural networks, particularly recurrent neural networks (RNNs) and convolutional neural networks (CNNs). These systems process audio through multiple stages: acoustic modeling converts sound waves into phonetic units, language modeling predicts word sequences, and decoding algorithms find the most likely text output. End-to-end models now combine these stages into unified neural architectures.

### Applications and Use Cases
Speech recognition powers virtual assistants (Siri, Alexa, Google Assistant), transcription services (medical, legal, media), voice-controlled devices (smart speakers, automotive systems), accessibility tools for people with disabilities, and customer service automation. The technology is also used in language learning applications, real-time translation services, and voice biometrics for security authentication.

### Current State and Future Directions
Contemporary speech recognition achieves accuracy rates above 95% in optimal conditions. Research focuses on improving robustness to noise, understanding context and intent, handling multiple speakers, and processing natural conversational speech. Emerging applications include real-time translation, emotion detection in speech, and integration with augmented reality interfaces.

### Related Technologies and Standards
Speech recognition is closely related to natural language understanding (NLU), which interprets the meaning of recognized speech, and text-to-speech (TTS) synthesis, which converts text back to audio. The technology interfaces with various audio file formats and standards for digital signal processing. Industry standards continue to evolve for interoperability between different speech recognition systems and applications.

## Schema Markup
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "speech recognition",
  "description": "automatic conversion of spoken language into text",
  "url": "https://en.wikipedia.org/wiki/Speech_recognition",
  "sameAs": [
    "https://www.wikidata.org/wiki/Q1454538",
    "https://en.wikipedia.org/wiki/Speech_recognition"
  ],
  "additionalType": "Academic discipline"
}

## References

1. [Source](https://github.com/JohnMarkOckerbloom/ftl/blob/master/data/wikimap)
2. [Source](https://www.pcworld.com/article/243060/speech_recognition_through_the_decades_how_we_ended_up_with_siri.html)
3. Freebase Data Dumps. 2013
4. Integrated Authority File
5. BBC Things
6. Quora
7. [Source](https://golden.com/wiki/Speech_recognition-W4WYV)
8. National Library of Israel
9. KBpedia
10. [Source](https://vocabs.ardc.edu.au/viewById/316)
11. [Source](https://vocabs.dariah.eu/tadirah/speechRecognizing)
12. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)
13. Wikibase TDKIV