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concept#Artificial Intelligence#Data#Integration#Security

Automatic Speech Recognition (ASR)

ASR refers to automatic conversion of spoken language into machine-readable text. It covers models, training data and system architectures for transcription and speech recognition.

Automatic Speech Recognition (ASR) is the automatic conversion of spoken language into text.
Established
High

Classification

  • High
  • Technical
  • Architectural
  • Intermediate

Technical context

Streaming APIs for audio captureNLU / dialog systemsTranscription storage and search indexes

Principles & goals

Data quality over model complexity: Good audio and annotation data improve performance most.Transparency and measurability: WER, latency and confidence must be measurable.Privacy by design: Anonymize sensitive audio data and limit access.
Build
Domain, Team

Use cases & scenarios

Compromises

  • Misinterpretation of sensitive content due to faulty transcription.
  • Privacy breaches if audio data is stored or shared improperly.
  • Overreliance on automatic outputs without human review.
  • Regular evaluation with representative domain data (WER split by speaker groups).
  • Use data augmentation to increase robustness to noise and accents.
  • Privacy-by-design: implement minimal data retention and access controls.

I/O & resources

  • Raw audio signal (WAV/FLAC/stream)
  • Annotated transcriptions for training
  • Vocabulary and language model data
  • Transcribed text
  • Timecodes and speaker labels
  • Quality metrics (WER, confidence)

Description

Automatic Speech Recognition (ASR) is the automatic conversion of spoken language into text. The concept covers models, training data, signal preprocessing and architectures for detection, segmentation and transcription of audio across accents, domains and noise conditions. Common applications include voice assistants, meeting transcription and captioning.

✔

  • Automated conversion of audio to text speeds up workflows and search.
  • Enables new interaction paradigms such as voice-driven systems.
  • Scalability in monitoring and analytics via text-based processing pipelines.

✖

  • Performance degrades in noise, overlaps or strong accents.
  • Domain-specific terms require adaptation or fine-tuning.
  • Language model bias can lead to worse results for underrepresented speakers.

  • Word Error Rate (WER)

    Standard metric measuring transcription accuracy (substitutions, insertions, deletions).

  • Real-Time Factor (RTF)

    Ratio of processing time to real-time audio duration; relevant for real-time requirements.

  • Latency (end-to-end)

    Time from arrival of audio signal to availability of the transcription.

Voice assistants (e.g., Siri, Alexa)

Large production systems that combine ASR with NLU to understand user requests and trigger actions.

Transcription workflows in contact centers

Automatic record creation and analysis of support calls for quality assurance and compliance.

Captioning of news broadcasts

Real-time or nearline transcription for captions and archiving of media content.

1

Define use case and specify latency/accuracy requirements.

2

Plan data collection, annotation and preprocessing.

3

Choose model, perform training/fine-tuning and evaluate using metrics.

4

Set up deployment (real-time/batch) and monitoring.

⚠️ Technical debt & bottlenecks

  • Undocumented feature pipelines for audio preprocessing.
  • Outdated models without automated retraining process.
  • Tight coupling between ASR components and downstream services.
Audio qualityDomain adaptationCompute resources
  • Storing automatic transcriptions of sensitive conversations without consent.
  • Using a general model in a specialized domain without adaptation.
  • Using ASR outputs as sole evidence in compliance cases.
  • Underestimating data annotation costs and time.
  • Neglecting continuous monitoring and model degradation in the field.
  • Lack of handling for multilingualism and code-switching.
Signal processing / audio feature engineeringMachine learning and model trainingPrivacy and compliance expertise
Application latency requirementsAvailability and quality of training dataPrivacy and compliance requirements
  • • Limited annotated data for specific domains
  • • Regulatory constraints on handling speech data
  • • Network or latency limits in real-time applications