The global market for speech recognition software, valued at US$3.5 billion in 2021, is estimated to grow to more than US$10.5 billion by 2030.
According to the forecasts managed by the Regional Research Reports firm, the market for voice recognition software will experience a compound annual growth (CAGR) of 20.12% over the next seven years. This will mean that, by 2030, its valuation will reach 10.5 billion dollars.
This confirms that, although voice recognition Technologys are still at an early stage, they are experiencing notable progress and, especially in recent times, have developed by leaps and bounds.
These Technologys include digital assistants, virtual assistants, virtual agents, and interactive agents. These have made it possible to go from a simple chat interface in which the user entered text and received a canned response, to a robust tool with which users can chat.
This advance is largely due to the rise of artificial intelligence (AI) and natural language processing (NLP) software, as well as improvements in computing.
Using machine learning and deep learning, speech recognition technology can intelligently grow to understand a larger vocabulary and colloquial language, as well as provide more accurate and correct responses to requests.
By providing information and performing specific tasks, whether external requests directed to the customer or internal requests directed to employees, speech recognition technology can augment the capabilities of humans.
Voice recognition software market prices
The price of voice recognition technology software is estimated to be between $100 and $350. This price depends on the features and specifications built into the software. The main features of the software include emotional intelligenceconversational skills, broad knowledge base, and personality.
Any software can come with its own set of challenges. Speech recognition technology, which is changing many industries and use cases (such as customer service and e-commerce), has some key issues that organizations need to be aware of.
- Preference for human agents. Although speech recognition technology is great for many tasks, in some contexts, such as those that require a significant amount of empathy, they may be better served by a human agent.
- transfers to humans. There may come a time when speech recognition technology does not have an answer to a user’s question. It is essential that the system is designed in a way that successfully solves this problem. Usually the best way to resolve this is to transition the user to a human agent.
In addition, Artificial Intelligence techniques such as PLN software help make speech recognition technology solutions easier to use and more powerful, providing more accurate results.
However, it must be borne in mind that speech recognition software is evolving rapidly and this will lead to a series of advances.
In general, users look for conversational interfaces to get answers to their questions. For example, they seek to query their data in a more natural way. Since the understanding of natural language has improved, people can talk to their data, find and explore information using a natural and intuitive language.
Voice will prove to be an important natural interface mediating human communication and device relationships.
With this technology, users can focus on discovering patterns and finding hidden meaning in data instead of memorizing SQL queries.
Likewise, voice will prove to be an important natural interface mediating human communication and relationships with devices and, ultimately, within an AI-driven world.
Artificial Intelligence is becoming a promising feature of many types of software. With machine learning, users can identify patterns in data, allowing them to make sense of content and help them understand what they see.
This pattern recognition is driving the rise of more powerful and context-aware speech recognition technology.
initial image | Andrea Piacquadio