By The Signal
If you have ever wondered how your smartphone can comprehend instructions like “Call Mom,” “Send a Message to Boss,” “Play the Latest Songs,” “Switch ON the AC,” then you are not alone. But how is this done? The one simple answer is Speech Recognition. Speech Recognition has gone through the roof in the recent 4-5 years and is making our lives more comfortable every day.
Speech Recognition was first introduced by IBM in 1962 when it unveiled the first machine capable of converting human voice to text. Today, powered by the latest technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning, speech recognition is touching new milestones.
This latest technological advancement is being used across the globe by top companies to make their user’s experience efficient and smooth. Technologies like Amazon’s Alexa, Apple’s Siri, Google Assistant, Google Speech, Google Dictate, Facebook’s Oculus VR, and Microsoft’s Cortana are all examples of Speech Recognition.
The expanding usage of speech-to-text technologies has also opened many new job domains, and students are wonderfully exploiting them. Many students are now joining courses like PGP in AI and Machine Learning after completing their graduation to improve their prospects. The high salary package of around INR 15 lakh for freshers is the 2nd biggest reason attracting students towards this, the biggest reason being the fantastic job role.
Speech Recognition was a very niche domain before the advent of AI and ML, which has completely transformed it now. Before we understand how AI and ML made changes, let’s understand the nuances of what all these terminologies are.
Artificial Intelligence is the technology by which machines become capable of demonstrating intelligence like humans or animals. Initially, AI was only about memorizing data and producing results accordingly; however, now it is much more than that as machines perform various activities like Speech Recognition, Object Recognition, Translating Texts, and a lot more.
Another latest addition to AI has been Deep Learning. With the help of Deep Learning, machines can process data and create patterns that help them make valuable decisions. This behavior of a machine through Deep Learning is similar to the behavior of a human brain. Deep Learning activities can be “Supervised,” “Semi-Supervised,” as well as “Unsupervised.”
Machine Learning is a subdomain of AI which teaches machines to memorize past events and activities. Through ML, machines are trained to retain various data sets’ information and outputs and identify patterns in these decisions. It allows the machine to learn by itself without the help of any programming code.
An example of Machine Learning is the e-Commerce websites suggesting products to you. The code, once written, allows machines to evolve on themselves and analyze user behavior and thus recommend products according to their preferences and past purchases. This involves Zero Human Interference and makes use of approaches like Artificial Neural Networks (ANN).
Speech Recognition is simply the activity of comprehending a user’s voice and converting that into text. It is chiefly of 3 types:
- Automatic Speech Recognition (ASR)
- Computer Speech Recognition (CSR)
- Speech to Text (STT)
Note: Speech Recognition and Voice Recognition are two different things. While the former comprehends a voice sample and converts it into a text sample, the sole purpose of the latter is to identify the voice and recognize to whom it belongs. Voice Recognition is often used for security and authenticity purposes.
How Has AI and ML Affected the Future of Speech Recognition?
The usage of Speech Recognition in our devices has grown considerably due to the developments in AI and ML technologies. Speech Recognition is now being used for tasks ranging from awakening your appliances and gadgets to monitoring your fitness, playing mood-booster songs, running queries on search engines, and even making phone calls.
The global market for Speech Recognition, currently growing at a Cumulative Annual Growth Rate (CAGR) of 17.2%, is expected to breach the $25 billion mark by 2025. However, there were enormous challenges initially that have been tackled with the use of AI and ML now.
When in its initial phase, some of the biggest challenges for Speech Recognition were Poor Voice Recording Devices, Huge Noise in the Voice Samples, Different Pitches in Speech of the Same User, etc. In addition to this, the changing dialects and grammatical factors like Homonyms were also a big challenge.
With the help of AI programs capable of filtering sound, canceling noise, and identifying the meaning of words depending on the context, most of these challenges have been tackled. Today, Speech Recognition shows an efficiency of 95%, which stood at less than 20% around 30 years back from now. The only biggest challenge remaining now for programmers is making machines capable of understanding emotions and feelings and satisfactory progress in this part.
The increasing efficiency in Speech Recognition is becoming an essential driving factor in its success, and top tech giants are leveraging these benefits. More than 20% of users searched on Google through Voice in 2016 only, and this number is expected to be far more prominent now. Businesses today are automating their services to make their operations efficient and introducing Speech Recognition facilities at the top of their to-do lists.
Some of the key usages of Speech Recognition today are listed below.
- The most common use of Speech Recognition is to perform basic activities like Searching on Google, Setting Reminders, Scheduling Meetings, Playing Songs, Controlling Synced Devices, etc.
- Speech Recognition is now also being used in various financial transactions, with some banks and financial companies offering the feature of “Voice Transfer” to their users.
Speech Recognition is no doubt one of the best innovations made by expanding technological developments. However, there is one thing to be noted if you are also planning to enter this sector. The domain is inter-mingled, and the mere knowledge provided by a Speech Recognition course won’t be enough for you to survive in this field.
Therefore, it is essential that you also sharpen your skills in allied concepts like Data Science, Data Analytics, Machine Learning, Artificial Intelligence, Neural Networks, DevOps, and Deep Learning. So what are you waiting for now? Hurry up and join an online course in Speech Recognition now!
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