Automatic Speech Recognition (ASR), also known as speech-to-text, is a great technology that helps computer systems and applications transcribe spoken language into written text. This technology uses a combination of expertise from linguistics, computer science, and electrical engineering.
Ever since their introduction into the world of technology, ASR systems have evolved rapidly. Early models were only able to recognize isolated words. Today, they have become highly advanced in understanding continuous speech, a variety of accents, and can even efficiently handle some background noise as well. Now, ASR is used in a wide range of applications, from live translation to voice assistants, and more.
There are multiple stages involved in the working of an ASR system. First, an audio input is captured and converted into a digital format, then its features, such as frequency or amplitude variations representing different sounds, are extracted, and then they are matched against acoustic models. These acoustic models are nothing but statistical representations of sounds learned from huge amounts of labeled speech data.
Students and professionals looking to make a career in data science must know how to build these kinds of data science models. In the following informative guide, USDSI® explains how to build an ASR system using PyTorch and the Hugging Face transformers library.
Download your copy now to learn how to build an ASR system from scratch.
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