Speech Recognition and Transcription Using AI

• An understanding of general AI and machine learning concepts
• Familiarity with audio or media file formats and tools

Audience

• Data scientists and AI engineers working with voice data
• Software developers building transcription-based applications
• Organizations exploring speech recognition for automation

Speech recognition and transcription using AI involves converting spoken language into written text through machine learning models and natural language processing systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level professionals who wish to implement, evaluate, and optimize AI-powered speech-to-text solutions for real-world use cases.

By the end of this training, participants will be able to:

• Understand how modern speech recognition models are trained and deployed.
• Evaluate open-source and commercial APIs for speech-to-text transcription.
• Handle multilingual and domain-specific transcription challenges.
• Build simple transcription workflows for different audio sources.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Overview of Speech Recognition Technologies

• History and evolution of speech recognition
• Acoustic models, language models, and decoding
• Modern architectures: RNNs, transformers, and Whisper

Audio Preprocessing and Transcription Basics

• Handling audio formats and sample rates
• Cleaning, trimming, and segmenting audio
• Generating text from audio: real-time vs batch

Hands-on with Whisper and Other APIs

• Installing and using OpenAI Whisper
• Calling cloud APIs (Google, Azure) for transcription
• Comparing performance, latency, and cost

Language, Accents, and Domain Adaptation

• Working with multiple languages and accents
• Custom vocabularies and noise tolerance
• Legal, medical, or technical language handling

Output Formatting and Integration

• Adding timestamps, punctuation, and speaker labels
• Exporting to text, SRT, or JSON formats
• Integrating transcriptions into apps or databases

Use Case Implementation Labs

• Transcribing meetings, interviews, or podcasts
• Voice-to-text command systems
• Real-time captions for video/audio streams

Evaluation, Limitations, and Ethics

• Accuracy metrics and model benchmarking
• Bias and fairness in speech models
• Privacy and compliance considerations

Summary and Next Steps