Listening - or hearing - is a natural human ability, next to seeing and talking. In other words, humans do not need to learn or practice hearing things. As young children, we start by hearing sounds around us: people talking, dogs barking, music being played… Growing a bit older, what we perceive through hearing turns into our understanding of language.
Although this process of language comes instinctively and naturally for humans, it is not the same for computers and machine systems, whose native language is machine code rather than words.
Language is considered one of the most complex forms of data, with different semantics and exceptions which are extremely difficult to understand in case of missing intent and context. Therefore, it is not a surprise that it has taken decades of our efforts to train AI to ‘learn’ human language. The good news is, machine learning’s capabilities are constantly developing, which enhances our opportunity to advance Natural Language Processing (NLP).
NLP can be seen as a branch of artificial intelligence (AI) that deals with the task of providing computers with the ability to understand spoken words and texts in relatively the same level human beings can.
During processing, language is divided into different parts by the NLP software to be interpreted and understood. This can be in the form of speech or text, depending on the software used. NLP integrates computational linguistics – rule-based modeling of human language – with statistical, machine learning, and deep learning models. These technologies combined allow computers to process human language in the form of either text or voice data, and to understand its meaning, including the intent and emotion.
NLP runs programs that translate languages, summarize large text data promptly, and respond to voice commands. Examples include voice-to-text dictation software, speech-operated GPS programs, customer support chatbots, digital assistants, and many other consumer convenience products. You may already be familiar with your Siri, Alexa, Google, or any other virtual assistant out there. Such technologies have taken us decades to develop thanks to advanced AI.
Moreover, NLP also plays an important role in business solutions that support the streamline effort of business operations, boost employee productivity, and reduce complexity of processes.
NLP is the driving force of AI in a lot of modern real-life applications. Examples of NLP solutions which StageZero are offering as services include language collection, data labeling, data categorization, verification and augmentation for both text and speech (audio) products.
As specialists in speech data creation for virtual assistants and other applications, we enable you to reach over 20 different small and medium languages throughout the world, which can be used for multiple projects, such audio recordings. Audio services involve having hundreds of users read and record sentences to improve voice recognition services. We give you access to over 10 million such users. These users can also be used for text labeling of handwriting, math, drawings, and more. Text labeling can improve functionality of chat bots, or give new insights into sentiment analyses of, for example, your brand perception among your customers.