Today, speech recognition is considered as one of the disruptive technologies that makes huge impact in banking, insurance, retail and manufacturing industries. The global market value of speech to text software is estimated to grow USD $456 million in 2014 to 1.33 billion by the year 2019 according to reports from Markets and Markets.
Speech recognition solutions and speech to text softwares enables natural, human-like conversations with machines and can achieve very high accuracy levels with appropriate fine tuning around vernacular attributes. By its enormous growth and rising popularity, speech to text software is becoming increasingly mainstream among different industries and enterprises. In India, where 70% of the country’s population is involved in the agriculture industry, speech technology has started been playing a critical role through user friendly speech solutions to rural farmers.
Features:
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Signals are ubiquitous across many research and development domains. Engineers and scientists need to process, analyze, and extract information from time-domain data as part of their day-to-day responsibilities. In a range of predictive analytics applications, signals are the raw data that machine learning systems must be able to leverage for the purpose of creating understanding and for informing decision-making.
For a company like Google or Amazon, hundreds of thousands of hours of spoken audio recorded in real-life situations is gold. That’s the single biggest thing that separates their world-class speech recognition system from your hobby system. The whole point of putting Google Now! and Siri on every cell phone for free or selling $50 Alexa units that have no subscription fee is to get you to use them as much as possible.
Main features of Signal Processing are:
Every single thing you say into one of these systems is recorded forever and used as training data for future versions of speech recognition algorithms and speech to text software.
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