Speech recognition refers to the process by which computer software translates human speech to a written, machine-readable format. This capability is used increasingly widely, in applications ranging from simple dictation and question-answering programs to tools for real-time foreign language translation and full-featured chatbots. Advanced speech recognition capabilities will be an important part of the future of computer software and mobile apps, allowing users to interact with software in an intuitive and hands-free way.

While the history of speech recognition dates back to the 1960s, progress in this field has accelerated greatly in the past decade with the advent of machine learning and deep learning. The use of these algorithmic approaches have opened the door for robust natural language processing (NLP), which goes beyond the simple conversion of spoken words into text and allows programs to understand the meaning of those words - and respond appropriately, if desired. NLP will be at the forefront of artificial intelligence (AI) applications, playing a key role in the functionality of helpful digital assistants and virtual agents as well as advanced robotics.

Speech recognition and natural language processing (NLP) programming skills are in high demand from a growing range of companies seeking to tap into this technology to create new products and services. Typically using Python programming and TensorFlow, machine learning and deep learning engineers with expertise in this field build models that analyze speech and language, discover contextual patterns, and produce insights and situationally appropriate responses. According to Glassdoor, NLP engineers earned an average annual salary of $114,121 as of November 2020.

Yes! Coursera has a variety of opportunities to learn about topics in machine learning and deep learning, including courses specifically on speech recognition and natural language processing (NLP). You can learn from top-ranked institutions in the field, like Stanford University and deeplearning.ai, leading companies like Google Cloud, or even by completing step-by-step tutorials alongside experienced instructors as part of Coursera’s Guided Projects. No matter how you choose to learn, you’ll be able to view course materials and complete assignments on a flexible schedule, which allows you to fit this valuable education in speech recognition into your existing studies, work, or family life.