The voice and speech recognition market size was valued at USD 14.22 billion in 2021 and is anticipated to reach USD 50.93 billion in 2030 growing at a compound annual growth rate (CAGR) of 15.23% from 2022 to 2030.
Natural language processing has found a number of important applications, one of which is speech recognition. Communication between human beings is mostly accomplished via the use of speech. People communicate their concepts and ideas to one another using a particular language. Computers are able to comprehend human language because to advancements in speech and voice recognition. The method of speech and voice recognition involves extracting the features of speech and voice and then categorising those traits based on how they match up with a dataset that has been previously recorded.
The process of recognising words or phrases and converting them into a format that can be read by a machine is known as automated speech and voice recognition. This process is also referred to as automatic speech recognition and voice recognition. By swapping the audio and text in the digital format, users would be able to manage the devices with their speech and voice rather than by utilising traditional input devices such as a keyboard, mouse, and other similar devices.
Speech and voice recognition have a broad variety of applications across a variety of sectors. Some of these applications include dictation and help software, while others may be found in contact centres, cellphones, and embedded devices. Systems that are controlled by voice have been implemented in a wide variety of contexts, including smart speakers, smart automobiles, and others. The results of a survey conducted by Adobe Analytics indicate that the most popular topics for voice searches conducted on smartphones or smart speakers are inquiries pertaining to music, the forecast, and amusing questions, followed by internet searches, news, and directions.
The expansion of the speech and recognition market will be fueled by an increase in the demand for voice and speech biometric systems as well as an increase in the usage of voice-based authentication in mobile apps. The need for speech and voice recognition systems would be driven by the increased usage of superior technologies such as AI, IoT, and machine learning. The speech and recognition market is expected to be driven in the next years by technologies such as voice recognition, which will be employed in developing technologies such as self-driving and autonomous automobiles, as well as speech and voice recognition in robots. In addition, the speech and voice recognition technology will play an essential part in the educational process for students who are visually impaired. This is one of the primary contributors that will be critical to the expansion of the international market.
The market is to be driven by emerging technical breakthroughs as well as the development of deep learning and neural network techniques. Classification algorithms are typically utilised in traditional speech and voice recognition systems to get the desired result. In various facets of speech and voice recognition, such as isolated word identification, audio-visual speech recognition, digital speaker recognition, and speaker adaptability, deep learning and neural networks have emerged as useful tools. The development of automated speech recognition (ASR) systems has been made possible by recent advances in deep learning and neural network methods. The development of end-to-end speech and voice recognition models has been significantly aided by the utilisation of deep neural networks as a primary driving factor.
Words that share a similar sound but have different meanings are referred to as homophones. Some examples of homophones are "right" and "write." Without a thorough language model and training on these terms with reference to the right contexts, artificial intelligence would have trouble identifying homophones in a phrase. There are a lot of words in English and Roman languages that may imply more than one thing. For instance, a "cell" might refer to a component of an organism, a chamber in a jail, or a region of radio coverage (cell phone). Additionally, heteronyms are frequent in most languages and can have more than one meaning. For instance, the word "close" may imply either "to shut" or "near" in English, while the word "converse" can mean either "to discourse" or "the opposite." As a result, it is possible that it will be difficult to determine whether it is appropriate to apply the appropriate homonyms while translating the material. The translator has to be fluent in both the language that is being spoken and the language that will be used to translate the content. Only then they will be able to solve this problem. Because of this, it's possible that the translator will need to have an in-depth expertise of both languages.
The purchasing habits of consumers are changing in both developed and emerging nations. From the comfort of their homes, people may browse items, inquire about prices and features, and even get tailored suggestions based on past purchases. With the use of voice assistants, this experience may be made much more seamless and interactive. When buying online, 41% of customers say a voice assistant is preferable to a website or app because it helps them to automate their regular purchasing activities. This finding comes from the Conversational Commerce Survey conducted by Capgemini in 2017. A few of the customer touchpoints where voice assistants can be helpful include searching for products and services, making shopping lists, adding items to shopping carts, making purchases, checking the status of orders, providing product and service feedback, using the customer support service, and recommending the product or service to other potential customers. Voice assistant application makers and service providers have a chance thanks to consumers' quicker adoption and use of voice assistants as well as an increase in online sales.
The voice and speech recognition market is segmented into the following categories: Function, Technology, Vertical and Region.
In 2021, the voice and speech recognition market held a commanding share of more than 65.5% of the global market in terms of revenue. Implementations of voice recognition are ideal for use in cars and mobile phones. Accessibility to data and services must be possible at all times and in all places due to society's growing mobility. The customer experience may be greatly improved by using cloud- and client-based voice recognition, and businesses can maximise cost savings.
Due to advantages like faster report turnaround and help for physicians with record keeping, this technology has also been supporting radiologists and doctors in managing patient records. The combination of voice recognition with Virtual Reality (VR) would increase market demand. For instance, in February 2017, Facebook improved the Oculus Rift VR platform by integrating voice recognition into the headset. The speech recognition market, on the other hand, is anticipated to see the quickest CAGR throughout the forecast period.
More than 72.00% of worldwide revenue in 2021 was accounted for by the non-artificial intelligence-based technology category. According to estimates, the market will continue to lead, increasing at a consistent CAGR between 2022 and 2030. The category of technology based on artificial intelligence, on the other hand, is anticipated to increase at the quickest rate throughout the projected period. As the system properly detects speech patterns, there is a growing need for artificial intelligence-based technologies.
By passing through a few stages—including the representation of speech units, formulation, and development of recognition algorithms, as well as the display of proper inputs—artificial intelligence exceptionally transforms speech into well-structured algorithms. The expansion of the artificial intelligence-based technology sector is anticipated to be aided by the expanding breakthroughs in machine learning and natural language processing. Over the forecast period, a growing number of digital assistants powered by artificial intelligence, including Alexa and Cortana, are anticipated to fuel demand for voice and speech recognition solutions.
The market has been further segmented based on verticals into the automotive, enterprise, consumer, BFSI, government, retail, healthcare, military, legal, education, and other sectors. In terms of revenue, the healthcare sector had the biggest percentage share in 2021—more than 29.00%. In EHR systems, speech recognition speeds up the data collection process. Through this approach, doctors are given the ability to communicate briefly with the system. Radiology, pathology, emergency medicine, and other areas of healthcare are among the areas where speech recognition is now being used.
Additionally, the automobile industry has a sizable market share. As car technology advance, linked gadgets will inform drivers of traffic conditions along the journey and possible detours. Due to the increasing adoption of connected devices and the increasing penetration of personal assistants like Google Home and Amazon Alexa in the Europe and Asia Pacific regions, voice and speech recognition technologies are anticipated to have a wide range of applications in the consumer and retail verticals over the course of the forecast period.
The largest portion of the worldwide revenue in 2021—more than 33.00% —was generated in North America. The region is anticipated to continue to grow at a constant CAGR and hold the leading market position during the projected period. The market in the North American area is anticipated to be driven by the rising usage of voice-enabled apps in smartphones and voice & speech recognition in mobile banking, consumer electronics, and IoT devices.
Due to the growing trend of linked devices in automotive and home automation, speech & voice recognition technologies are anticipated to have greater usage in the consumer electronics and retail industries in Europe. In China, Japan, and Singapore, there is a rising need for speech and voice recognition, which is anticipated to drive market expansion throughout the Asia Pacific region. The expansion of the APAC regional market is also anticipated to be aided by the increasing adoption of voice-enabled devices in the automotive and healthcare sectors.
|Report Coverage||Revenue Forecast, Competitive Landscape, Growth Factors, Environment & Regulatory Landscape and Trends|
Verint introduced its low-code conversational AI product, the Verint Virtual Assistant (IVA), in April 2021. IVA can quickly transform the current conversation data into automated self-service experiences. It enables business experts to swiftly create a chatbot that is ready for production to divert calls and assist clients. Businesses may increase capabilities throughout the organisation with Verint IVA's limitless voice and digital intelligence.
In order to incorporate ambient clinical intelligence (ACI) into Microsoft Teams and scale virtual consultations targeted at improving physician wellbeing and patient health outcomes, Microsoft and Nuance Communications announced Nuance Dragon Ambient eXperience (DAX) in September 2020.
An automated voice recognition service called Amazon Transcribe Medical was launched by Amazon Web Services in December 2019. It will assist developers in adding medical dictation and documentation to their products.
Watson Assistant, a smart enterprise speech recognition and assistant system driven by AI, cloud, and IoT, was introduced by IBM in March 2018.