AI Speech Recognition Training is a practical two-day program designed to help learners understand how artificial intelligence enables speech recognition and transcription systems This course focuses on how spoken language is converted into text using AI, covering speech data processing, transcription workflows, accuracy optimization, multilingual support, and real-world applications such as call analytics, voice assistants, media transcription, and accessibility solutions.

• Understand AI speech recognition concepts
• Learn speech-to-text processing basics
• Understand voice data workflows
• Gain knowledge of language recognition techniques
• Learn real-time speech processing
• Understand conversational AI integration
• Explore voice automation applications
• Identify speech AI use cases

• AI and machine learning beginners
• Developers exploring speech and voice technologies
• Product managers working on voice-enabled solutions
• Media, transcription, and content professionals
• Technology professionals adopting speech AI systems

• Basic understanding of artificial intelligence or machine learning concepts
• Familiarity with audio, speech, or media content is beneficial
• General awareness of data processing concepts
• Interest in voice-based and speech-driven AI applications

• Understanding core concepts of speech recognition and transcription
• Awareness of speech-to-text workflows and AI models
• Knowledge of accuracy, language, and noise-handling considerations
• Evaluating real-world speech recognition use cases
• Applying ethical and responsible practices in speech AI systems

By the end of this training, participants will be able to explain how AI-powered speech recognition systems work, understand transcription workflows and accuracy challenges, evaluate real-world applications, and apply ethical and responsible practices when deploying speech-to-text solutions.

Introduction to Speech Recognition and AI
• Overview of speech recognition and transcription systems
• Difference between speech recognition, voice AI, and audio AI
• Key components of speech-to-text pipelines
• Common use cases and industry adoption

Speech Data and Audio Processing Fundamentals
• Audio signals, sampling, and speech characteristics
• Noise, accents, and speech variability challenges
• Feature extraction concepts for speech recognition
• Preparing audio data for transcription

Speech Recognition Models and Transcription Workflows
• Automatic speech recognition fundamentals
• Real-time versus batch transcription
• Handling punctuation, timestamps, and speaker separation
• Measuring transcription accuracy and performance

Multilingual, Domain-Specific, and Scalable Transcription
• Multilingual and accent-aware transcription
• Domain adaptation and specialized vocabularies
• Transcription for meetings, calls, and media
• Scaling speech recognition systems

Applications and Integration Scenarios
• Call analytics and customer support transcription
• Media, podcast, and video transcription
• Accessibility and assistive technologies
• Integrating speech recognition into applications

Ethics, Privacy, and Future Trends
• Privacy, consent, and audio data protection
• Bias and fairness in speech recognition systems
• Responsible deployment of transcription AI
• Future trends in speech and voice technologies

• Speech recognition fundamentals
• Speech-to-text concepts
• Audio preprocessing techniques
• Language and voice recognition basics
• Real-time speech processing
• Conversational AI workflows
• Voice automation concepts
• NLP integration basics
• Accuracy and performance considerations
• Practical speech AI scenarios

• Conceptual understanding assessments
• Speech transcription use case analysis exercises
• Accuracy and quality evaluation activity
• Final knowledge evaluation quiz

Participants will receive course materials, slides, reference materials, exercises and access to resources for further learning.

Participants who successfully complete the training will receive an AcadNXT Certification in AI Speech Recognition Training, validating their expertise in understanding speech recognition concepts, transcription workflows, ethical considerations, and real-world applications.