“Alexa, play my favorite song!” … because this is where the music is playing. Intelligent assistants such as Alexa, Siri and Co. have now become an integral part of our everyday life and almost seem human. But what is actually happening in the background? How is it possible that a machine can be controlled by my speech and play music, play my favorite series or do my shopping?
One of the underlying technologies is what is known as Natural Language Processing , or NLP for short. This defines itself as a sub-discipline of artificial intelligence (AI) and deals with the machine processing of natural language. Natural language sees itself as the language used by people in everyday life and is therefore different from e.g. B. Programming languages. Language appears in a wide variety of unstructured data forms and can be processed differently accordingly. This includes speech in audio format or text format.
How does NLP work?
Of course, Alexa and Co. are not real people, but they have learned from us. Machine learning ( ML ) or deep learning methods are used to teach the machine natural language.
Imagine a toddler just learning to speak. They point to a tree and at the same time make the sounds that produce the spoken word “tree”. The child visually perceives the properties of the tree and at the same time the spoken sounds. Repeat this a few times and the child will learn that there is a connection between the visual properties of the tree and the spoken word. In the simplest form of ML, the process is very similar. You give the machine an audio track and an accompanying transcript. Through huge data sets, the machine learns which audio properties are related to which written words and is thus able to transcribe spoken language.
Of course, these are simplified examples; the spectrum of natural language is many times more complex, but the basic principle of machine learning remains the same.
What is possible with NLP
As varied and complex as languages are, so are the areas of application of NLP. Below we give an overview of the most common technologies and how they can create added value.
Speech-to-text algorithms are used for the classic transcription of spoken language into text format. Speech-to-text is often only the first building block in a chain of NLP algorithms, since text formats are much better suited for further analysis.
Speech-to-text makes sense wherever you have large amounts of spoken language in the form of audio data, such as in call centers, customer meetings or video interviews. Transcription opens up many more options for text analysis, as well as documentation and archiving options.
Would you like to know how your customers comment on your brand on social media and whether the online reviews of your brand are positive or negative? Would you like to better understand the call center interactions with your customers and be able to better respond to customer inquiries? Then the sentiment analysis technology is the right one for you. Emotions and moods in communication are analyzed in sentiment analysis. The technology analyzes the language for emotions and moods that resonate. This allows you e.g. B. Find out what attitudes customers have towards your brand and take appropriate measures to influence them.
Classification is about assigning text to specific categories. Here, too, there are countless possible applications; you will come across many of them in everyday life. If your e-mail provider automatically moves e-mails to the spam folder, this has happened on the basis of an automated analysis of the content and a corresponding classification.
Classification algorithms can be trained on all possible categories. They help to keep an overview of the flood of text information that we are confronted with every day. A few examples of this:
- Automated assignment of incoming mail or support requests to the relevant subject areas (e.g. advertising, requests, invoices, orders) or clerks
- Clear overview and detailed analyzes of documents in your archive or document management system (e.g. classification in contracts, terminations, etc.)
- In-depth analyzes of (customer) communication (e.g. which complaints occur more frequently, what do the customers like, etc.)
Chatbots are dialog systems and probably come closest to intelligent assistants like Alexa. The complexity and “intelligence” of these machines can vary greatly. They often draw on a variety of other NLP technologies and suggest a conversation to the user. Chatbots are often used as the first point of contact for support and customer inquiries. Depending on the medium, they also use speech-to-text and text-to-speech algorithms. With the help of classification, the question or concern of the customer is identified and answered based on rules.
Text to Speech
Text-to-speech technology is the counterpart to speech-to-text: Speech in the form of text is converted into a mechanical audio track. This technology is used in particular in dialogue systems, such as chatbots over the phone or smart assistants.
So what happens linguistically when I ask Alexa to play my favorite music? As in many applications, several technologies are linked here in order to meet the customer’s wishes. The customer’s speech is transcribed, analyzed (classified), compared with the database and executed with an additional text-to-speech comment “OK, I’ll play your favorite song xyz”.
Watch-outs: what to look out for?
- Machines are only as intelligent as the data they learn from. if e.g. For example, if a speech-to-text algorithm is only trained with audio data spoken in High German, it will have problems understanding strong accents correctly. So if the training data sets are incorrect or incomplete, this will also be reflected in the result.
- Understand what an algorithm can and cannot do: When it comes to AI, many people think of machines that, like in films, can act and think almost like humans. The reality is different. Algorithms are usually designed for a very specific task. Smart assistants can play our favorite music, but cannot drive the car independently. You should be aware of this so that you can use NLP in a targeted manner.
- Language is multi-layered and complex. In some applications it is also worthwhile to use other data sources, e.g. B. Use facial expressions and body language from video data to get an even deeper understanding of communication.
The areas of application of NLP are as complex as language itself. With the right approach and the right data, great potential in communication, data analysis, processes and innovation can be promoted.
If you have any questions on this topic or would like to talk shop about use cases without obligation, do not hesitate to write to us.
PS: NLP has nothing to do with Neuro-Linguistic Programming, which is also abbreviated as such.
- Vom 28. April 2022