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Future of Image and Speech Recognition with Machine Learning
The repetitive style of ML is essential for interactive models such as image and speech recognition where it can easily apply knowledge and experience from an extensive collection of data repositories.
FREMONT, CA: One of the significant challenges that the current research community is trying to address is how to equip the machines to recognize, process, and infer decisions from sounds and visuals. A lot of technologies are powering the research works. However, machine learning (ML) is a promising technology that is expected to impart the highest value to a range of interactive real-world applications such as image and speech recognition.
The repetitive style of ML is essential for interactive models as they can adapt independently when exposed to new data sets. ML can easily apply knowledge and experience from an extensive collection of data repositories to allow face recognition, speech recognition, and much more.
ML is increasingly being used in image recognition, especially in case of the digital image where the measurements state the outputs of each pixel in the image. Based on the variety, the inputs have to be categorized.
• For image/face detection, the categories can be Face and No Face present. There might be a different category for each person.
• For character identification, a piece of writing can be segmented into smaller images containing a single character each. The types can range from 26 letters of the English alphabet to the 10 digits and even special characters.
Google is currently using ML technology in its products such as Google Search, Google Drive, Google Photos, and the list goes on, for improved image detection through the keyword inputted by the user.
Speech recognition (SR) involves the translation of speech into text. It is also popularly called as automatic speech recognition (ASR). In speech recognition, the purpose of a software application is to recognize the spoken words and might even use a set of numbers that represent the speech signal. SR applications include a voice user interface such as call routing, voice dialing, and domotic appliance control.
Baidu’s research and development department have developed a tool called Deep Voice using ML. The tool is capable of delivering artificial voices that are akin to a real human voice.
Apart from the image and audio recognition, ML is also adding value in other sectors, especially medical analysis, classifying, arranging, forecasting, and data analysis.