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Speech recognition: History, Applications, Technology & Products

Speech recognition (automatic speech recognition (ASR), computer speech recognition, or speech-to-text (STT)) is a sub-field of computational linguistics concerned with methods and technologies that translate spoken language into text or other interpretable forms.

Language: English [EN]
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Speech recognition topic overview

The analysis highlights History, Applications, Technology and Products as prominent areas in the source structure around Speech recognition.

Related topics
233
Source areas
6
Connected nodes
239
Extracted relationships
275
Concept neighborhoods
55
Bridge connections
239

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Overview · 70 topics
Applications · 54 topics
History · 54 topics
Models, methods, and algorithms · 33 topics
Further information · 16 topics
Performance · 6 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

History

Models, methods, and algorithms

Applications

Performance

Further information

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Speech recognition connects Entity context

The extracted context around Speech recognition shows recurring relationship patterns in the source. For example, Speech recognition → Advanced, Andrew, Annie, Applications, Australia, Automatic Speech Recognition, Beat, Building Computers That Understand, Business Media, Cambridge Studies, Cambridge University Press, Clare-Marie, Cole, Conversational Interface Technologies, David, December, Distant Speech Recognition, Efficient Controller-free Text Entry, Emerging Applications, Ergonomics Another extracted example is Speech recognition → Applications, Building Computers That Understand, Computer Speech, Deep Learning Approach, Deng, DNN-based, Doug O'Shaughnessey, Dynamic, Frederick JelinekSpoken Language Processing, Fundamentals, IntroductionAutomatic Speech Recognition, Jurafsky, Language Processing, Lawrence Rabiner, Learning, Li Deng, Machine, Manfred, Martin, Methods. Use these groups to spot repeated connection types before inspecting the individual relationships.

Speech recognition

Top relations

related to Further reading · 77
Speech recognition → Advanced, Andrew, Annie, Applications, Australia, Automatic Speech Recognition, Beat, Building Computers That Understand, Business Media, Cambridge Studies, Cambridge University Press, Clare-Marie, Cole, Conversational Interface Technologies, David, December, Distant Speech Recognition, Efficient Controller-free Text Entry, Emerging Applications, Ergonomics
related to Books · 31
Speech recognition → Applications, Building Computers That Understand, Computer Speech, Deep Learning Approach, Deng, DNN-based, Doug O'Shaughnessey, Dynamic, Frederick JelinekSpoken Language Processing, Fundamentals, IntroductionAutomatic Speech Recognition, Jurafsky, Language Processing, Lawrence Rabiner, Learning, Li Deng, Machine, Manfred, Martin, Methods
related to 1970–1990 · 26
Speech recognition → Baker, BBN, Carnegie Mellon, CMU, DARPA, Defense Analysis, During, HMM, HMMs, IBM, ICASSP, Institute, James Baker, Janet, Leonard Baum, Markov, Massachusetts, Newton, Philadelphia, Raj Reddy's
related to Pre-1970 · 26
Speech recognition → Audrey, Bell Labs, Biddulph, Davis, Flanagan, Fumitada Itakura, Funding, Gunnar Fant, IBM's, James, John, Linear, Nagoya University, Nippon Telegraph, Pierce, Previous, Raj Reddy, Reddy's, Shoebox, Shuzo Saito
related to Education · 13
Speech recognition → Also, Amira Learning, Assessing, Automatic, CALL, CAPT, CEFR, Common European Framework, In, Languages, Microsoft Teams, Pronunciation, Reference
related to Hidden Markov models · 12
Speech recognition → An HMM, Each, Fourier, Gaussians, HMM, HMMs, In, Markov, Speech, The, The HMM, These
related to Software · 11
Speech recognition → Android, APIs, Commercial, Common Voice, Coqui STT, Gboard, HTK, Microsoft Windows, Speech, Sphinx, TensorFlow
related to Performance · 10
Speech recognition → Accuracy, Command Success Rate, CSR, Other, Single Word Error Rate, Speech, SWER, The, Vocalizations, WER
related to Security · 9
Speech recognition → Alexa, Attackers, For, One, Speech, The, They, Two, Voice-controlled
see also · 7
Speech recognition → AI, Language Tags, Language TranslatorAutomotive, LinuxSpeech, NaturallySpeakingFluency Voice TechnologyGoogle Voice, SearchIBM ViaVoiceKeyword, VoiceXMLVoxForgeWindows Speech Recognition List

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

speech recognition language used voice systems use model applications speaker learning system neural models words many word using deep hmm

Speech recognition relationships Subject–Predicate–Object triples

TTTA extracted 275 structured relationships around Speech recognition. Examples in this analysis include Speech recognition → is a → multi-level pattern recognition task.Acoustic signals are structured into a hierarchy of units and 10 → instance of → where n is an integer. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Speech recognitionis amulti-level pattern recognition task.Acoustic signals are structured into a hierarchy of units0.90text
10instance ofwhere n is an integer0.80text
individual phonemesinstance ofin spite of their effectiveness in classifying short-time units0.80text
isolated wordsinstance ofin spite of their effectiveness in classifying short-time units0.80text
early neural networks were rarely successful for continuous recognition because of their limited ability to model temporal dependencies.One approach was to use neural networks for feature transformationinstance ofin spite of their effectiveness in classifying short-time units0.80text
or dimensionality reductioninstance ofin spite of their effectiveness in classifying short-time units0.80text
intonationinstance ofsometimes with inconsequential prosody0.80text
pitchinstance ofsometimes with inconsequential prosody0.80text
tempoinstance ofsometimes with inconsequential prosody0.80text
rhythminstance ofsometimes with inconsequential prosody0.80text
and stressinstance ofsometimes with inconsequential prosody0.80text
Microsoft Teamsinstance offor example in products0.80text

Related concept clusters Concept neighborhoods

The concept neighborhoods around Speech recognition bring nearby vocabulary together. In this analysis, examples include Speech, Used and Use. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Speech recognition
    • Speech
    • Used
    • Use
    • Language
    • Systems
    • Speaker
    • Using
    • Words
    • Neural
    • Voice
    • Technology
    • Vocabulary
  • speech recognition
    • Speech
    • Use
    • Vocabulary
    • Used
    • Speaker
    • Language
    • Systems
    • Voice
    • Neural
    • Using
    • Words
    • Technology
  • voice recognition
    • Speech
    • Commands
    • Applications
    • Systems
    • Use
    • Vocabulary
    • System
    • Include
    • Speaker
    • Language
    • Recognition
    • Voice
  • translating speech
    • Used
    • Use
    • Systems
    • Speaker
    • Using
    • Words
    • Neural
    • Voice
    • Technology
    • Vocabulary
    • Many
    • Learning
  • global autonomous language exploitation
    • Model
    • Processing
    • Pronunciation
    • Speaker
    • Speech
    • Also
    • Technology
    • Recognition
    • Models
    • Software
    • Hmm
    • May
  • speech corpus
    • Used
    • Use
    • Systems
    • Speaker
    • Using
    • Words
    • Neural
    • Voice
    • Technology
    • Vocabulary
    • Many
    • Learning
  • deep neural networks
    • Networks
    • Neural
    • Learning
    • Deep
    • Models
    • Accuracy
    • Researchers
    • Time
    • Use
    • One
    • Many
    • Recognition
  • speech technology
    • Used
    • Use
    • Systems
    • Speaker
    • Using
    • Words
    • Neural
    • Voice
    • Technology
    • Vocabulary
    • Many
    • Learning

Connections between topic areas Semantic bridges

For Speech recognition, one of the stronger structural bridges in this analysis connects Speech recognition with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Speech recognition — Overview · splits 169 ⟂ 71
Speech recognition — History · splits 185 ⟂ 55
Speech recognition — Applications · splits 185 ⟂ 55
Speech recognition — Models, methods, and algorithms · splits 206 ⟂ 34
Speech recognition — Further information · splits 223 ⟂ 17
Speech recognition — Performance · splits 233 ⟂ 7

Map overview Semantic statistics

Speech recognition

Nodes240
Edges239
Triples275
Avg. degree1.99
Density0.008333
Components1

Source & methodology

TTTA analyzes the structure around Speech recognition to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as History, Applications, Technology & Products, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Speech recognition · EN edition · Analysis: TopicsToTalkAbout

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