Speech-to-text normalization for French, German, and Spanish

Inverse text normalization (ITN) converts spoken ASR output into written formats suitable for databases, entity recognition, and other downstream systems, and is presented as particularly important for French, German, and Spanish because their number, date, currency, and grammar conventions differ substantially from English and from one another. French requires handling vigesimal number forms and regional variants, German requires reversing unit-before-tens compounds and applying local decimal, date, and time conventions, while Spanish requires recognizing gendered hundreds forms and selecting locale-specific currency formatting. The discussion compares low-latency rule-based weighted finite-state transducers, more contextual but resource-intensive transformer approaches, and hybrid tagger-plus-WFST systems, arguing that language-specific rules and normalization within or immediately after ASR improve downstream parsing and prevent silent data errors. It also notes that code-switching requires rules to change at language boundaries, domain-specific terminology may need custom overrides, and reference and hypothesis transcripts must use consistent normalization for meaningful word error rate evaluation. The source promotes Gladia’s managed and open-source normalization offerings, along with its Solaria models, while outlining configuration, pricing, diarization, and customer-data policy details.

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