Voice recognition technology is increasingly making its mark on daily life and has long since ceased to be the domain of science fiction. Thanks to the development of voice recognition, potential applications in enterprises are expanding every year. Voice and speech recognition systems are being used in an increasing number of industries. Voice recognition – how can it impact your enterprise?
We use voice and speech recognition every day, for example, when using smartphones. They have built-in software that can process spoken words into text. However, this is not the only application of voice recognition. It is expanding, which is particularly visible in the areas of IT and automation. Speech and voice recognition allow for the acceleration and automation of many processes in enterprises. For example, in warehouses, the use of a voice interface frees up the hands of employees, who can control machines using voice commands. Voice recognition is also used in
cRPA – cognitive robotic process automation. Combined with other technologies (such as AI), processes are automated and can self-improve, which translates into better efficiency and higher profits.
How does voice recognition technology work?
First, it should be noted that voice recognition and speech recognition are not the same thing. In the first case, we are talking about technology that analyzes a person’s voice timbre to, for example, react only to sentences spoken by them. In the case of speech recognition, the software does not pay attention to the timbre of the voice or accent, but identifies and analyzes the words. How does voice recognition work? It analyzes a person’s voice timbre by comparing the heard message with existing samples. To prevent activation by others, voice recognition devices most often activate after a specific phrase is spoken, the best example of which are smart speakers from companies like Amazon or Google. Voice recognition is used in software designed for speech recognition. The biggest challenge is creating a system that will not only identify a specific person’s voice but also understand the words they are speaking. Modern speech recognition systems combine achievements in computer science, linguistics, and engineering. Voice recognition software extracts speech features, determines their vectors, and then decodes and transforms them into words. Various algorithms are used to process them, such as:
- natural language processing, which is based on interactions between humans and machines; these transform words into formal symbols that are identified by the software;
- N-grams, which determine the probability of words or phrases occurring; an N-gram is a sequence of n-words, e.g., the sentence “let’s use voice recognition” is a 3-gram; the combination of grammar and probability allows for the recognition of sequences of words and sentences;
- hidden Markov models, which also rely on probability and assume that it depends on the current state; they are used to create models for labeling individual parts of speech, which are mapped, allowing for the determination of the probability of individual words and sentences occurring;
- neural networks, which are combined with other types of algorithms; they work similarly to human brains and use input and output data, as well as weights and thresholds, to learn human speech.
Voice recognition is becoming widespread in many companies. Enterprises are digitizing work environments, which often happens in the spirit of
hyperautomation. This is a strategy aimed at automating all possible processes and transforming the enterprise into an optimized and efficient one. In business automation, speech and voice recognition find wide application. One example of its use is voicebots, which are already capable of effectively replacing consultants. In this case, the use of voice recognition often combines features of AI and IA – artificial intelligence and intelligent assistance systems, respectively. Voice recognition is often a component of
cRPA – speech recognition software is able to not only handle current customer service but can also propose new solutions. The combination of voice AI and chatbots that can learn behaviors, and consequently predict them, can accelerate the customer service process in an enterprise, which will also allow for better matching of offers. Speech recognition technology is already used in banks and e-commerce.
Benefits of implementing voice recognition technology in business
Implementing voice recognition technology can improve company efficiency. The most visible advantage of voice recognition is the ability to unlock employee potential. Handing over customer service to voicebots and delegating employees previously responsible for it to other tasks allows, firstly, for increased efficiency (machines do not get tired), and secondly, it improves morale and increases creativity. Another benefit is the improvement of security levels. A well-trained machine that reacts only to the voices of specific people is able to protect company resources from breaches. An additional advantage of speech recognition software is the ability to transcribe speech to text faster, as programs will always be able to do this faster than a human can type. Speech recognition programs are also used in medicine and the military. Voice commands issued by specialists are interpreted by the voice interface faster and more efficiently than would be possible by typing commands, which in both cases can be life-saving. Voice recognition is also used in education, mainly in language learning. This can also be reflected in business, where it can serve to improve employees’ language skills.
The biggest challenges of voice recognition technology
Although modern speech recognition systems are becoming better, they are not perfect. Relatively speaking, voice recognition programs work best in English. Market monopolists in this area work mainly in this language, which provides a large database. The structure of English also has an impact, which can be problematic for others. For example, in Slavic languages, declension and syntax can be transformed more freely, which means that building dictionaries can exceed the capabilities of the system. Another problem is understanding speech and the context of statements. Although the use of increasingly complex algorithms enables better understanding of speech, voice recognition systems can have trouble with different accents, speech tempo, or emotional coloring. The acoustic environment is also a difficulty. If it is full of noise and signal interference in the form of, for example, microphone defects, voice recognition devices may have reduced effectiveness. Another challenge is the relatively low trust in this type of technology. As a PWC report states, one-third of respondents are afraid of using speech recognition software
[1]. On the other hand, from a business point of view, relatively high implementation costs are also an obstacle, but it is predicted that they will decrease with each passing year.
[1] https://www.pwc.com/us/en/services/consulting/library/consumer-intelligence-series/voice-assistants.html 
