// AI DATA CAPABILITIES / INVERSE TEXT NORMALIZATION

Turning spoken language into structured text.

Inverse Text Normalization — the critical post-processing layer that makes your speech AI output production-ready.

When a speech recognition system transcribes "three hundred and forty-five dollars and fifty cents", your downstream system needs to read "$345.50". That transformation from spoken form to written form is Inverse Text Normalization (ITN), and it is one of the most technically demanding, language-specific challenges in speech AI. Nextura.ai delivers ITN as both a human annotation service and a rule-based / ML-assisted pipeline — across 30+ global languages.

// WHAT.ITN.COVERS

ITN transformation categories

Numbers & Numerals"four hundred and twenty" → "420"; "three point five" → "3.5"
Currency"twenty five dollars and ninety nine cents" → "$25.99"
Dates"the fourteenth of march twenty twenty six" → "14/03/2026"
Times"three forty five in the afternoon" → "3:45 PM"
Phone Numbers"zero nine one four five six seven eight nine" → "09145-6789"
Addresses"twelve baker street london" → "12 Baker Street, London"
Abbreviations"mister sharma" → "Mr. Sharma"; "doctor" → "Dr."
Measurements & Units"thirty five kilometers per hour" → "35 km/h"
Percentages"forty five point three percent" → "45.3%"
Domain-Specific EntitiesMedical, legal, financial, and technical terminology normalization
// LANGUAGES

Multilingual ITN across 30+ foreign languages.

ITN is deeply language-specific — the rules for English are entirely different from German, Mandarin, or Tamil. Nextura.ai's service is built natively for each language.

  • Language-specific numeral systems (Arabic numerals, Devanagari, CJK characters, etc.)
  • Cultural date and time formats (DD/MM/YYYY vs MM/DD/YYYY vs YYYY/MM/DD)
  • Currency symbol conventions and regional formatting rules
  • Transliteration handling for romanized Indic and CJK language transcriptions
  • Code-switching normalization for bilingual or multilingual speech
English (all variants)FrenchGermanSpanishMandarinJapaneseKoreanPortugueseArabicHindiTamilTeluguBengaliMarathi+ 35 more
// DELIVERY.APPROACH

Human-in-the-loop + pipeline automation.

  • Rule-based ITN pipeline: deterministic normalization for high-frequency patterns
  • ML-assisted ITN: context-aware normalization for ambiguous or domain-specific cases
  • Human annotation review: expert review layer for complex or low-resource language ITN
  • Custom glossary and entity dictionary integration for brand, product, and domain terms
  • Integration-ready output: formatted for downstream ASR post-processing, TTS systems, NLU pipelines