The speech recognition technology has come a long way after\nthe launch of the first speech recognition machine by IBM. As the exponential\ngrowth of technology and machine learning, using speech recognition has\nincorporated in our daily lives. With the everyday use of voice-driven\ntechnologies such as Apple\u2019s Siri, Amazon\u2019s Alexa, or Microsoft\u2019s Cortana, the\nvoice-driven applications are making the human lives easier. Every new\nvoice-interactive device that humans use in their daily lives, restores our\nfaith in machine learning and Artificial Intelligence. <\/p>\n\n\n\n
First coined in the year 1956 by John McCarthy, artificial\nintelligence can be defined as \u201chuman intelligence exhibited by machines\u201d. Artificial\nIntelligence was first used to analyze and quickly compute data, it now allows\ncomputers to perform tedious tasks that generally only humans were capable of.<\/p>\n\n\n\n
Machine learning is a known subset of artificial intelligence,\nrefers to systems that can naturally learn by themselves. It involves teaching\na computer to analyze and recognize patterns, rather than programming it with\nspecific rules. The training process involves feeding large amounts of data to\nthe algorithm and allowing it to learn from that specific data and identify\npatterns. In the early days, the developers would have to write code for every\nobject they wanted to recognize like human and dog, now a single system can\nrecognize both by showing it many examples of each. As a result, the rise of\nArtificial Intelligence continues to get smarter over time without human\nintervention. <\/p>\n\n\n\n
There are several techniques and approaches to machine\nlearning. One of those approaches is artificial neural networks, an example of\nwhich is product recommendations. Ecommerce companies often use artificial\nneural networks to suggest products the customers are more likely to purchase.\nMany ecommerce stores can do this by ingesting data from all of their users\u2019\nbrowsing experiences and utilizing that information to make effective product\nrecommendations.<\/p>\n\n\n\n
A few common applications of artificial intelligence today include\nobject recognition, translation, speech recognition, and natural language\nprocessing. Temi is an audio-to-text app powered by automated speech\nrecognition (ASR) and natural language processing (NLP). This is the conversion\nof spoken word to text while NLP is the processing of the text to derive its\nmeaning. Since humans often speak in complex phrases, abbreviations, or\nacronyms, it takes detailed computer analysis of natural language to produce\naccurate transcription.<\/p>\n\n\n\n
The challenges related to speech recognition technology are\nnumerous but they are narrowing. It includes overcoming bad recording\nequipment, background noises, difficult accents and dialects as well as the\nvaried pitches of people\u2019s voices.<\/p>\n\n\n\n
Teaching a machine to analyze and read a spoken language as\nhumans do is something remarkable but haven\u2019t been perfected. Listening to and\nunderstanding the words a person says is so much more than hearing the words\nthe person speaks. When it comes to humans, they also read the person\u2019s eyes,\ntheir current facial expressions, body language, and the tones and inflections\nin their voice. Another nuance of speech is the human tendency to shorten\ncertain words. For instance, I don\u2019t know\u201d becomes \u201cdunno\u201d, we have used\nabbreviated words for so long, that we do not pronounce them as precisely as\nwhen we learned them. This human disposition strikes yet another challenge for\nmachine learning when it comes to speech recognition.<\/p>\n\n\n\n
The developers at Temi have leveraged decades of research\nand development in speech recognition to create an automated transcription\nservice that is super fast, easy-to-use, and affordable. We would not have been\nable to build Temi without all the foundations in speech recognition from other\ncompanies. <\/p>\n","protected":false},"excerpt":{"rendered":"
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