Speech recognition is a complex mélange of linguistics, mathematics and statistics. Also known as speech-to-text, it attempts to identify spoken words to then process human speech into written format. To do so in the most natural and precise way, AI and ML are used to integrate grammar, syntax, structure, and composition of audio and voice signals to best understand & process human speech.
When it comes to actually doing the work, different projects have different speech recognition requirements, which play a role when it comes to selecting the most adequate features to suit these specific needs. Some of the common features of speech recognition are:
Speech recognizers are composed of various components: there is speech input, feature extraction, feature vectors, a decoder, and a word output. Or in simpler terms, speech recognizers make use of algorithms to help with the interpretation of spoken words into text by following these steps:
This fourth step is done by the decoder, which leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.
In terms of quality metrics, speech recognition is measured based on its accuracy rate. Aspects such as pronunciation, accent, pitch, volume, and background noise all nuance the word error rate found in possible output, thus both acoustic and language models must be taken into consideration:
Thus, AI and ML help with improving accuracy, through the implementation of various algorithms and computation techniques to recognize speech into text. The most commonly used ones are the following:
Speech recognition can serve many benefits, but in order to do a good job at it, you need high-quality training data, where diversity is key.
Through the TAUS HLP Platform, we are able to provide this data for your specific speech recognition project needs, with the help of our community of workers. Get in touch with us to receive more information about our speech recognition services.
Pamela is the Marketing & Training Coordinator at TAUS. With her background as a Communication Science student, she aims to finds the best ways to engage users both on social media channels as well as occasional blog articles.
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