Catalog
concept#Artificial Intelligence#Machine Learning#Analytics#Data

Speech Recognition

Automatic conversion of spoken language into text using acoustic and language models.

Speech recognition converts spoken language into machine-readable text using signal processing, acoustic models and language models.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Cloud ASR APIs (e.g., Google, AWS, Azure)Transcription and editorial systemsStreaming platforms and players

Principles & goals

Privacy by design: minimize and process sensitive audio locally.Error and uncertainty handling: explicit use of confidence scores.Domain adaptation: adapt language models to vocabulary and phrasing.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Misunderstandings from mis-transcriptions with business impact.
  • Privacy breaches from improper audio storage.
  • Bias in training data can disadvantage marginalized speakers.
  • Measure and improve audio quality early (preprocessing).
  • Use hybrid workflows: ASR plus editorial post-editing.
  • Introduce monitoring with WER and latency metrics in production.

I/O & resources

  • Raw audio (streaming or file)
  • Speech and domain data for model training
  • Metadata (language, speaker ID, context)
  • Transcribed text
  • Time-coded segments
  • Quality and confidence metrics

Description

Speech recognition converts spoken language into machine-readable text using signal processing, acoustic models and language models. It is applied in virtual assistants, dictation systems and large-scale transcription services. Key challenges include accents, background noise, latency and user privacy.

✔

  • Increased efficiency by automating time-consuming transcription tasks.
  • Improved accessibility via captions and voice interfaces.
  • Enables new interaction modes (voice UX) and data for analytics.

✖

  • Performance degradation with strong dialects or very noisy environments.
  • High compute requirements for high-quality models.
  • Language- and domain-specific vocabularies require adaptation.

  • Word Error Rate (WER)

    Measures transcription accuracy as the proportion of incorrect words.

  • Latency (end-to-end)

    Time between speech input and availability of transcript output.

  • Confidence distribution

    Distribution of confidence scores to estimate need for manual correction.

Google Speech-to-Text (example)

Cloud service for transcription and real-time ASR across many languages.

Kaldi in research projects

Open-source toolkit for acoustic modeling and ASR pipeline research.

Transcription workflow in newsrooms

Hybrid process of automatic transcription with editorial post-editing.

1

Define requirements: set latency, privacy, domain constraints.

2

Build and evaluate a prototype with a generic model.

3

Perform domain adaptation and integrate into production pipeline.

⚠️ Technical debt & bottlenecks

  • Outdated models without a regular retraining strategy.
  • Fragmented integrations to multiple ASR providers without abstraction.
  • Missing monitoring for quality regressions in production.
Audio quality and noise levelCompute and memory resources for modelsAvailability of domain-specific training data
  • Using cloud ASR for sensitive customer calls without encryption.
  • Replacing human moderation in safety-critical contexts.
  • Ignoring bias testing before production deployment.
  • Underestimating effort for domain-specific data collection.
  • Lack of handling low-confidence segments in the workflow.
  • Undefined SLOs for latency and accuracy.
Knowledge in signal processing and audio engineeringExperience with ML/ASR models and data annotationEngineering skills for system integration and scaling
Latency requirements for real-time interactionPrivacy and compliance requirementsQuality requirements for recognition rate and robustness
  • • Network latency or lack of connectivity in offline mode
  • • Legal requirements for retention of audio material
  • • Limited on-device resources (CPU, RAM)