Speech Recognition Training in Auckland, New Zealand

This training program on Speech Recognition focuses on the techniques, tools, and frameworks used to develop and implement speech recognition systems. Participants will explore key concepts, including voice data processing, speech-to-text algorithms, and the use of modern tools such as Google Speech API, IBM Watson, and DeepSpeech. This course combines theoretical knowledge with practical exercises, preparing participants to build applications such as voice assistants, transcription systems, and real-time speech recognition models.

Introduction to Speech Recognition and Preprocessing

Session 1: Introduction to Speech Recognition Algorithms

• Basic components: Acoustic models, language models, and decoders.
• Overview of traditional speech recognition models: Hidden Markov Models (HMM).
• Modern speech recognition using Deep Learning (RNNs, LSTMs).

Session 2: Introduction to Speech Recognition

• • What is speech recognition? Key concepts and applications.
  • Overview of speech recognition systems: From audio input to text output.
  • Types of speech recognition: Automatic Speech Recognition (ASR), voice command systems, etc.

Session 3: Speech Data Preprocessing

• • Audio file formats (WAV, MP3, FLAC, etc.) and their use in speech recognition.
  • Data cleaning and conversion: Handling noise and silences in speech data.
  • Feature extraction methods: MFCC, spectrograms, and their role in speech recognition.

Building Speech Recognition Applications and Advanced Techniques

Session 4: Using Speech Recognition Libraries

• • Overview of popular speech recognition libraries: Google Speech API, IBM Watson, DeepSpeech.
  • Setting up and using APIs for speech-to-text applications.

Session 5: Advanced Techniques in Speech Recognition

• • Introduction to end-to-end speech recognition systems using deep learning.
  • Understanding language models for better accuracy.
  • Real-time speech recognition and continuous speech-to-text conversion.

Session 6: Deployment and Best Practices

• • Optimizing speech recognition models for production.
  • Handling challenges such as background noise, accents, and various languages.
  • Deployment options: Cloud-based and on-premise solutions.
  • Future trends in speech recognition and ethical considerations.
  • Hands-on

This course offers practical, hands-on learning that enables participants to develop and deploy effective speech recognition applications, while also providing a solid theoretical foundation for understanding the underlying technologies.