NLP & LANGUAGE

Speech-to-Text (STT)

NLP & Language

Definition

Speech-to-text is the technology that converts spoken language into written text. Modern systems achieve accuracy above 95% even in noisy environments. It is used to transcribe meetings, support calls, document dictation, and voice commands in factories. Combined with NLP, it enables automatic analysis of conversation content.

Related terms

NLP & Language

Text-to-Speech (TTS)

Text-to-speech is the technology that converts written text into synthetic speech. Modern systems produce natural and expressive voices, practically indistinguishable from human ones. Companies use it for intelligent telephone IVR systems, voice assistants, accessibility, and automatic narration of internal training content.

NLP & Language

NLP (Natural Language Processing)

NLP is the field of AI concerned with the interaction between computers and human language. It includes tasks such as sentiment analysis, entity extraction, machine translation, and text generation. For SMEs, NLP enables automating the analysis of emails, customer reviews, legal documents, and internal communications.

NLP & Language

Conversational AI

Conversational AI encompasses all technologies that enable natural interactions between humans and machines via voice or text. It goes beyond a simple chatbot: it manages multi-turn conversations, maintains context, understands nuances, and can execute actions. It includes voice assistants, intelligent chatbots, and automated customer service agents.

EXPLORE

More terms in NLP & Language

NLP (Natural Language Processing)

Sentiment Analysis

Sentiment analysis is the NLP technique that automatically determines the emotional tone of a text: positive, negative, or neutral. Companies use it to monitor customer reviews, analyze brand sentiment on social media, and quickly identify satisfaction issues. It can be applied to thousands of texts in seconds.

Named Entity Recognition (NER)

NER is an NLP technique that automatically identifies and classifies entities such as names of people, companies, locations, dates, and monetary amounts within a text. In business, it is used to extract key information from contracts, invoices, emails, and legal documents, saving hours of manual data entry work.

Tokenization

Tokenization is the process of splitting a text into smaller units (tokens) that the AI model can process. Different models use different strategies: some split by words, others by subwords. Understanding tokenization helps estimate API costs (calculated per token) and optimize prompts to stay within the context window limits.

BERT

BERT (Bidirectional Encoder Representations from Transformers) is a language model developed by Google that understands the context of a word by looking both left and right in the text. It is particularly effective for comprehension tasks such as classification, question answering, and semantic search. Many enterprise search systems use BERT behind the scenes.

Text Classification

Text classification is the task of automatically assigning one or more categories to a document. Practical applications include: routing support tickets by priority, cataloging emails by department, classifying accounting documents by type. Modern models achieve accuracy above 95% on many business classification tasks.

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