Speech recognition refers to a computer interpreting the words spoken by a person and converting them to a format that is understandable by a machine. Depending on the end-goal, it is then converted to text or voice or another required format.
For instance, Apple’s Siri and Google’s Alexa use AI-powered speech recognition to provide voice or text support whereas voice-to-text applications like Google Dictate transcribe your dictated words to text. Voice recognition is another form of speech recognition where a source sound is recognized and matched to a person’s voice.
Speech recognition AI applications have seen significant growth in numbers in recent times as businesses are increasingly adopting digital assistants and automated support to streamline their services. Voice assistants, smart home devices, search engines, etc are a few examples where speech recognition has seen prominence. As per Research and Markets, the global market for speech recognition is estimated to grow at a CAGR of 17.2% and reach $26.8 billion by 2025.
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Speech Recognition and Artificial Intelligence
Speech recognition is fast overcoming the challenges of poor recording equipment and noise cancellation, variations in people’s voices, accents, dialects, semantics, contexts, etc using artificial intelligence and machine learning. This also includes challenges of understanding human disposition, and the varying human language elements like colloquialisms, acronyms, etc. The technology can provide a 95% accuracy now as compared to traditional models of speech recognition, which is at par with regular human communication.
Furthermore, it is now an acceptable format of communication given the large companies that endorse it and regularly employ speech recognition in their operations. It is estimated that a majority of search engines will adopt voice technology as an integral aspect of their search mechanism.
This has been made possible because of improved AI and machine learning (ML) algorithms which can process significantly large datasets and provide greater accuracy by self-learning and adapting to evolving changes. Machines are programmed to “listen” to accents, dialects, contexts, emotions and process sophisticated and arbitrary data that is readily accessible for mining and machine learning purposes.
Speech Recognition and Natural Language Processing
Natural language processing (NLP) is a division of artificial intelligence that involves analyzing natural language data and converting it into a machine-readable format. Speech recognition and AI play an integral role in NLP models in improving the accuracy and efficiency of human language recognition.
From smart home devices and appliances that take instructions, and can be switched on and off remotely, digital assistants that can set reminders, schedule meetings, recognize a song playing in a pub, to search engines that respond with relevant search results to user queries, speech recognition has become an indispensable part of our lives.
Plenty of businesses now include speech-to-text software to enhance their business applications and streamline the customer experience. Using speech recognition and natural language processing, companies can transcribe calls, meetings, and even translate them. Apple, Google, Facebook, Microsoft, and Amazon are among the tech giants who continue to leverage AI-backed speech recognition applications to provide an exemplary user experience.
Use Cases of Speech Recognition
Let’s explore the uses of speech recognition applications in different fields:
- Voice-based speech recognition software is now used to initiate purchases, send emails, transcribe meetings, doctor appointments, and court proceedings, etc.
- Virtual assistants or digital assistants and smart home devices use voice recognition software to answer questions, provide weather news, play music, check traffic, place an order, and so on.
- Companies like Venmo and PayPal allow customers to make transactions using voice assistants. Several banks in North America and Canada also provide online banking using voice-based software.
- Ecommerce is significantly powered by voice-based assistants and allows users to make purchases quickly and seamlessly.
- Speech recognition is poised to impact transportation services and streamline scheduling, routing, and navigating across cities.
- Podcasts, meetings, and journalist interviews can be transcribed using voice recognition. It is also used to provide accurate subtitles to a video.
- There has been a huge impact on security through voice biometry where the technology analyses the varying frequencies, tone and pitch of an individual’s voice to create a voice profile. An example of this is Switzerland’s telecom company Swisscom which has enabled voice authentication technology in its call centres to prevent security breaches.
- Customer care services are being traced by AI-based voice assistants, and chatbots to automate repeatable tasks.
Other industries that are actively investing in voice-based speech recognition technologies are law enforcement, marketing, tourism, content creation, and translation.
Global Impact of Speech Recognition in Artificial Intelligence
Speech recognition has by far been one of the most powerful products of technological advancement. As the likes of Siri, Alexa, Echo Dot, Google Assistant, and Google Dictate continue to make our daily lives easier, the demand for such automated technologies is only bound to increase.
Businesses worldwide are investing in automating their services to improve operational efficiency, increase productivity and accuracy, and make data-driven decisions by studying customer behaviours and purchasing habits.
AI has facilitated an exponential growth in a wide range of sectors of the global economy. It is estimated that AI’s contribution to the global economy will hit $15.7 trillion in 2030, which is significantly higher than China and India’s combined output.
The future of speech recognition is tremendously noteworthy. As per reports, Apple has plans to launch the Siri-controlled Apple TV, there will be a rise in smart wearable devices like watches, earbuds, jewellery, and voice-based software that are being programmed to identify the context of user requests to provide enhanced support.
As speech recognition and AI impact both professional and personal lives at workplaces and homes respectively, the demand for skilled AI engineers and developers, Data Scientists, and Machine Learning Engineers, is expected to be at an all-time high.
There will be a requirement for skilled AI professionals to enhance the relationship between humans and digital devices. As job opportunities are created, they will result in increased perks and benefits for those in this field.
As per PayScale, the average salary for an Artificial Intelligence professional in India today is ₹15 lakh. Furthermore, the field offers lucrative career advancement opportunities, both financially and profile-wise. However, this requires investing in an Artificial Intelligence course to master Data Science and learn to create intuitive, human-like software solutions using real-time data.
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What are the difficulties in speech recognition in AI?
Speech recognition is translating the spoken word into written form. The problem with this, is that there are few distinct languages in the world and it is all based on the phonetic systems that were created back when there was no technology to rely on. The way we speak, in natural speech, is not a phonetic language, but a distinct speech system. Speech sounds can overlap, and that is a problem with computers, because they don't understand what is going on. They are programmed by people to understand the unique ways of speaking, but this method is not effective.
How does speech recognition work?
Speech recognition is the process of converting spoken words into machine readable data. This can be done by either good old rule-based approaches or by applying machine learning techniques. Rule-based approaches have been used in computers for speech recognition since the 60s. They are initially trained by hand and require a lot of effort to maintain over time. Machine learning approaches, on the other hand, are trained automatically from a set of training data and require little maintenance over time. They are therefore more efficient in the end, although initial training is often quite expensive.
What is the purpose of speech recognition?
The purpose of speech recognition is to understand the voice of the speaker and the meaning of the spoken words. Speech recognition has the potential to replace the keyboard and make it unnecessary to type on the computer. Speech recognition technology has been around for about 30 years now, and it's constantly improving. Speech recognition technology is more popular today than ever, since it's being integrated into more and more devices. For example, computers now have speech recognition software that lets users dictate their letters and reports instead of typing them. This saves time and energy, and it gives you a hands-free device to work with.