「speech recognition」の32件の結果

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.‎

The skills and experience you might need to already have before starting to learn speech recognition are right in front of you, in the form of your mobile device. You may already be using speech to text your friends and family. This is one of the clearest examples of speech recognition, and voice-to-text is an experience you are likely familiar with. You may also want to learn about voice-activated assistants, like Alexa, Siri, and Google Assistant. These three offer the latest technologies that may help you learn speech recognition.‎

The kind of people that are best suited for roles in speech recognition are those technology professionals who understand how software drives speech recognition. Software creators write programs to process human speech into written words on a screen. Speech recognition works as it focuses on translating speech from a verbal format to a text-based format. These people who are best suited for roles in speech recognition likely are also well versed in or want to learn about programming languages and artificial intelligence.‎

How do I know if learning speech recognition is right for me? You may know if learning speech recognition is right for you if you have a deep passion to work on the cutting edge of technology. Speech recognition technology has fast changed the way modern society communicates, and there is likely a great deal more learning that will come in this area. You may also want to use your software skills to write code to train computers in similar ways to how we train our own brains, using fundamental processes, innovative thinking, and data research. If you’re interested in learning speech recognition as a career, you might know that this is a field that is right for you.‎

Some topics that are related to speech recognition may include natural language processing (NLP), deep learning neural networks, machine learning, and artificial intelligence. You may also want to dive into each of the main speech recognition technologies offered by the major tech companies like Amazon, Google, Apple, Facebook, and others.‎