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 Duration 14 hours

Course Outline

Overview of Speech Recognition Technologies

  • Historical development and progression of speech recognition capabilities
  • Fundamental components: acoustic modeling, language modeling, and decoding processes
  • Contemporary architectural approaches, including RNNs, transformers, and Whisper

Audio Preprocessing and Transcription Fundamentals

  • Managing various audio file formats and sample rate specifications
  • Techniques for audio cleaning, trimming, and segmentation
  • Converting audio to text: comparing real-time processing with batch operations

Practical Application of Whisper and External APIs

  • Deployment and operational use of OpenAI Whisper
  • Utilizing cloud-based services (Google, Azure) for transcription services
  • Assessment of performance metrics, latency factors, and cost structures

Linguistic Diversity, Accents, and Sector-Specific Adaptation

  • Processing diverse languages and regional accents in government and public contexts
  • Implementation of custom vocabularies and strategies for noise resilience
  • Managing specialized terminology in legal, medical, or technical domains

Output Structure and System Integration

  • Incorporating timestamps, punctuation, and speaker identification labels
  • Exporting data into standard formats such as plain text, SRT, or JSON
  • Integrating transcription outputs into existing applications or database systems

Implementation Workshops for Public Sector Use Cases

  • Transcribing official meetings, interviews, and public audio content
  • Development of voice-to-text command systems for operational efficiency
  • Generation of real-time captions for video and audio streams for accessibility

Performance Evaluation, Constraints, and Ethical Considerations

  • Definition of accuracy metrics and benchmarking methodologies for models
  • Addressing bias and ensuring fairness in speech recognition algorithms
  • Adherence to privacy standards and regulatory compliance requirements

Summary and Recommended Follow-Up Actions

Requirements

  • A foundational understanding of general artificial intelligence and machine learning principles
  • Proficiency with audio or media file formats and associated technical tools

Target Audience

  • Data scientists and AI engineers engaged with voice data analysis
  • Software developers creating applications reliant on transcription services
  • Public sector organizations exploring speech recognition for operational automation for government workflows

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