Speech Recognition Training in San Francisco

Speech Recognition Training in San Francisco, USA

This course focuses on the techniques, tools, and frameworks used to develop and implement speech recognition systems.

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Speech Recognition Training is a professional training program delivered by ProgNXT in San Francisco, USA. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this Speech Recognition Training course with hands-on, instructor-led sessions. This training program on Speech Recognition focuses on the techniques, tools, and frameworks used to develop and implement speech recognition...

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: VTN46
14 Hrs
Last Updated: April 2026

Course Rating5/5

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.

Welcome to the official Speech Recognition Training certification program in San Francisco, USA. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Participants should have:

Basic programming knowledge, preferably in Python.
Familiarity with machine learning concepts (e.g., supervised learning, neural networks).
No prior experience in speech recognition is required, but knowledge of audio processing would be beneficial.

By the end of this training, participants will:

Understand the basic principles and components of speech recognition.
Learn about various speech-to-text algorithms and frameworks.
Be proficient in data preprocessing for audio files, including feature extraction techniques.
Gain hands-on experience in implementing speech recognition systems using popular tools and libraries.
Understand language models and acoustic models used in speech recognition.
Develop basic speech-to-text applications and explore use cases such as voice assistants and real-time transcription.

Participants will gain the following skills:

Speech data preprocessing: Handling and converting audio files for use in machine learning.
Feature extraction: Implementing methods like MFCC (Mel Frequency Cepstral Coefficients) and spectrograms.
Building and training speech-to-text models using machine learning techniques.
Using APIs for speech recognition, such as Google Speech API and IBM Watson.
Deploying real-time speech recognition applications in Python.

Participants will be assessed on:

Speech Data Preprocessing: Ability to handle and prepare audio data for model training.
Knowledge of speech-to-text algorithms and their implementation.
Ability to use speech recognition libraries like DeepSpeech, Google Speech API, or IBM Watson.
Proficiency in building basic speech-to-text applications.
Application of concepts through hands-on projects (e.g., transcription systems, voice assistants).

Upon successfully completing the training and assessments, participants will receive a ProgNXT Certificate of Completion, validating their proficiency in Speech Recognition.

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.

Participants will receive:

Comprehensive training slides covering the theory and practical aspects of speech recognition.
Python notebooks with pre-written code examples and exercises.
Sample datasets for audio file processing and training.
Links to resources such as tutorials, documentation, and research papers on speech recognition.
Code repositories with additional examples and best practices.

Participants will be evaluated through a hands-on project and a final quiz.
ProgNXT Certificates of completion will be awarded to participants who successfully complete the assessments.

Speech Recognition Training Events in san-francisco

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More Upcoming Session Dates

October 2026

🌐 Online Instructor-Led

23rd Oct 2026→24th Oct 2026

📍 San Francisco, USA

24th Oct 2026→25th Oct 2026

November 2026

🌐 Online Instructor-Led

2nd Nov 2026→3rd Nov 2026

📍 San Francisco, USA

3rd Nov 2026→4th Nov 2026

🌐 Online Instructor-Led

24th Nov 2026→25th Nov 2026

📍 San Francisco, USA

25th Nov 2026→26th Nov 2026

December 2026

🌐 Online Instructor-Led

11th Dec 2026→12th Dec 2026

📍 San Francisco, USA

12th Dec 2026→13th Dec 2026

🌐 Online Instructor-Led

25th Dec 2026→26th Dec 2026

📍 San Francisco, USA

26th Dec 2026→27th Dec 2026

Events for other courses in San Francisco, USA

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Speech Recognition Training in San Francisco