Natural language processing services

Summarization

It takes a lot of effort to determine what a text document is truly about, even when it is well-organized and searchable, and the majority of longer texts don’t come with a convenient TL;DR or summary. Fortunately, AI is rapidly improving at understanding the key ideas the author was attempting to convey and at generating a summary of the text itself.

The classification of the text by topic allows us to quickly search through the documents, while summarization allows us to quickly grasp the information contained in the text. Everyone who has to deal with large volumes of text, from analysts to executives, will benefit greatly from this.

Sentiment analysis

Languages are very nuanced, so while some of the information is factual, like a judgement that the restaurant’s food was bad, other parts are emotive, like the man’s outrage about the food’s quality.

Machines are generally not very good at reading emotions, but with the aid of AI, they can pick up cues that indicate the sentiment attached to a segment of text, showing whether the content is happy, sad, or evokes any other sentiment the model is trained to recognize.

This allows our clients to better understand their customer feedback, adjust chatbot responses, and react to prevailing sentiments regarding specific topics reflected in the comments. It has also been used to understand sentiment concerning specific topics in the media and on social media. In this case, NLU and sentiment analysis are used in tandem to recognize topics of interest and subsequently interpret sentiments about them.

Speech recognition

When communicating information, people typically speak rather than write. This is especially true for certain forms of communication, such as meetings, hearings, calls, etc. Some of these can be processed as raw audio, but for best results, speech must be converted to text to facilitate navigation, analysis, and post-processing using NLP methods.

At MindTitan, we’ve spent years developing speech recognition solutions for both widely spoken languages like English and lesser-known ones like Estonian. We also specialize in working with compromised audio, such as phone calls and noisy surroundings or phone calls made in noisy surroundings.

We help our clients extract information from their speech data, either by using our existing speech recognition solutions or by developing custom models for their specific use cases, such as a specific recording environment, domain-specific vocabulary, regional accents, etc.

Speech synthesis

Speech recognition allows a machine to understand human speech, but speech synthesis is what is needed to help the machine respond in kind.  Machine learning has advanced significantly in the synthesis of natural-sounding voices in recent years.

With just a few hours of training data, we create speech synthesis models that generate natural-sounding speech using custom voices. These models help our clients automate customer calls and provide prompt and personalized messages to users, for example, in the event of a service outage.

However, speech synthesis has a wider range of applications and is actively used to create audio versions of written articles and even audio books. Although the quality isn’t quite as good as when it’s read by someone with a deeper understanding of the material and a wider cultural context, it’s still a useful tool.

Speaker identification

Knowing who is speaking is often as crucial as understanding their message. This can be very useful for meeting memos, automatic subtitling, customer identification, and even for law enforcement needs.

Speaker recognition is one area where machines very often outperform humans, not only in recognition accuracy but also in the number of people these systems can accurately identify. As with humans, this is, of course, strongly affected by the quality of the sound. For example, speaker recognition in a phone call is inherently less accurate than in a recording of a meeting.

Emotion detection

Emotions can be determined from audio in a similar fashion to sentiment analysis in text. This is even more important here since speech recognition itself is imperfect, and people focus less on the tone of words when voice can be used.

This is particularly important when speaking with customers over the phone since it helps to understand how strongly they feel about the reason for the call, or when contacting emergency services because the emotion in the caller’s voice can indicate the urgency of the call.