Speech Recognition Market

The speech recognition industry is experiencing rapid revenue growth because of increased application in the education sector. Furthermore, the increased need for correct and simple-to-use speech recognition APIs appropriate for a wide range of sectors and languages drives market expansion. Speech-to-text industries that perceive rapid digitization have a beneficial impact on market share. Additionally, other industries undergoing digital transformation boost the value of the speech recognition market. The speech recognition market is impacted by various factors. The main factors driving the growth of the speech recognition services market include the growing demand for voice authentication in mobile banking applications and the growing impact of artificial intelligence (AI) on the accuracy of speech recognition. However, the high cost of the high-end speech recognition system is expected to hinder the growth of the global market. Nevertheless, the development of speech recognition software for micro-linguistics and local languages is expected to present lucrative growth opportunities for the players in the global speech recognition market.

The incorporation of speech-enabled in-car infotainment systems is gaining traction around the world as various countries implement "hands-free" legislation that controls the usage of mobile phones while driving. Speech product developers are focused on innovations that are projected to accelerate market growth during the forecast period. The use of speech recognition technology in smartphones allows doctors and clinicians to translate their voices into a thorough clinical description that is saved in the Electronic Health Record (EHR) system. The introduction of speech recognition in healthcare technology has eased the process of interacting with EHRs, allowing clinicians to save hours per day and up to $50,000 per year. As a result, firms that incorporate speech recognition into their services have better prospects for growth and development, as their personnel has more time to focus on what matters. Shortly, the market will be driven by the increasing penetration of voice-enabled IoT devices in smart home automation. IoT-enabled devices would enhance a variety of typically offline devices by providing creative user interactions in addition to standard ways such as touch displays and buttons.

Automobiles and mobile phones are ideal platforms for speech recognition systems. Because of current societal increasing mobility, data and services had to be accessible at all times and from any location. Cloud and client-based speech recognition applications can greatly improve the customer experience while also saving enterprises money. Furthermore, because of benefits such as reducing report turnaround time and supporting doctors in record keeping, this technology has been assisting doctors and radiologists in maintaining patient data. The integration of speech recognition with Virtual Reality (VR) is projected to increase market demand. For example, in February 2017, Facebook improved its VR platform, Oculus Rift, by adding speech recognition to the oculus rift's VR gear. The speech recognition segment, on the other hand, is expected to grow at the quickest rate over the forecast period.

The last two years have been some of the most exciting and anticipated in the long history of Automatic Speech Recognition (ASR), with the release of many enterprise-level fully neural network-based ASR models (e.g., Alexa, Rev, Assembly AI, ASAPP, etc). Many reasons contribute to the faster success of ASR deployments, including a growing ecosystem of publicly available toolkits, more open-source datasets, and a growing interest in the ASR challenge among engineers and researchers. This convergence of factors has resulted in a shift in momentum in commercial speech recognition.

These advancements not only improve existing applications of speech recognition concerning the aforementioned, categories but have also helped in enhancing the accuracy of smart speakers such as Siri and Alexa. As speech recognition accuracy improves in noisy circumstances it has resulted in facilitating numerous market opportunities in the recent past. For instance, it is used by various police body cameras to automatically record and transcribe exchanges in developed economies. This has also helped in keeping track of essential contacts to recognize potentially harmful interactions and alerting respective authorities with help of speech-to-text transcripts.

Furthermore, the integration of speech recognition systems in the OTT platform has provided automated subtitles for live videos which allow individuals and stakeholders to watch live information. These use-case has been adopted by OTT players such as YouTube, LinkedIn, Amazon Prime Videos, Netflix, and others. With a higher rate of accuracy speech recognition solution is gaining traction and is expected to expand with an impressive growth rate as a result of ascending digitalization.

Companies are collaborating with digital platforms such as Google Assistant and Amazon Alexa to produce market-appropriate solutions, ultimately stimulating market growth. Some of the established applications of speech recognition assistants in this field include purchasing groceries, clothing, homecare, and electronic products, as well as ordering meals from restaurants. According to the Capgemini Digital Transformation Institute's Conversational Commerce Report, 2018, about 51% of consumers in the United States, United Kingdom, France, and Germany are already utilizing voice assistants via smartphones (81%). Furthermore, consumers in the aforementioned countries have accepted voice assistant for a variety of functions, including 82% for information seeking, 67% for music playing, 35% for purchasing products such as groceries, homecare, home furnishing, and clothes, 52% for purchasing electronics, and 56% for ordering meals. Increased use of voice assistants in such applications presents tremendous growth potential for speech recognition technology.

The use of speech biometric technologies has been accelerated by the persistently rising demand for high-level security solutions, particularly among BFSI consumers, to provide effective risk management and combat instances of fraud and identity theft. The technology improves individualized client experiences by offering simple and safe authentication for a variety of applications, including mobile banking authentication, e-banking or app-based transaction security, and transparent conversational authentication. This technology is advancing at a rapid pace. According to the Capgemini Digital Transformation Institute's Conversational Commerce Report, 2018, approximately 28% of active banking and insurance service customers in the United States, United Kingdom, France, and Germany are currently using a voice assistant to make a transaction. A growing number of banking industry organizations are adopting speech recognition assistants for a variety of purposes. For example, Capita One, a pioneer in promoting speech recognition technology in the BFSI sector, released an Amazon Alexa Skill that allows customers to access account information and complete transactions using voice commands in a quick, precise, and secure manner.

As speech technology advances, developers and engineers strive to overcome obstacles associated with speech systems. Background noise, punctuation, accent, fluency, speaker identification, and technical words/jargon are all common factors that impede the smooth operation of speech recognition solutions. One of the most difficult issues in voice is achieving accuracy in languages other than American English. According to the Speechmatics Voice report, accent and dialect concerns will account for approximately 30.4% and 21.2% of all complaints in 2021, respectively.

Voice-based technologies will continue to provide more personalized experiences as they improve at differentiating and identifying users' voices. However, the threat to speech data privacy persists, impeding the expansion of the speech and speech recognition market share. The most significant impediment to the adoption of speech recognition technology has been identified as accuracy. Background noise can be a significant hurdle when attempting to increase the accuracy of a speech recognition model. During the speech recognition process, it encounters numerous background disturbances such as cross-talk, white noise, and other distortions that can interfere with speech detection. Another big problem is making speech recognition operate with several languages, accents, and dialects. There are about 7000 languages spoken worldwide, with an infinite number of accents and dialects. English alone has over 160 dialects spoken throughout the world. No speech recognition system can cover them all. Even aiming for compatibility with only a few of the most widely spoken languages might be difficult. Accent or dialect concerns provide a substantial barrier to the adoption of speech recognition technology.