We present WikIPA, a new multilingual benchmark designed for automatic speech-to-IPA (STIPA) transcription. By integrating human-curated IPA transcriptions from WikiPron with spoken recordings and metadata from Lingua Libre, WikIPA connects textual phonetic representations with real speech across 78 languages. This open resource supports both broad (phonemic) and narrow (phonetic) transcription tasks, enabling fine-grained evaluation of multilingual phonetic transcription systems. WikIPA provides over 289,000 paired entries and serves as a large-scale foundation for STIPA. We benchmark several state-of-the-art STIPA systems, including MultIPA, (Lo)WhIPA, and ZIPA. Results show that ZIPA achieves the lowest mean error rates across most languages, outperforming Whisper- and Wav2Vec-based baselines. Error analyses reveal that remaining discrepancies largely stem from minor phonetic confusions rather than complete transcription failures, emphasizing the challenge of modeling fine-grained articulatory variation. WikIPA thus establishes the first systematic, multilingual evaluation framework for speech-to-IPA transcription and highlights the potential of combining open, community-driven resources to advance STIPA evaluation.

Automatic Speech Recognition systems (ASR) based on neural networks achieve great results, but it remains unclear which are the linguistic features and representations that the models leverage to perform the recognition. In our study, we used phonological syllables as tokens to fine-tune an end-to-end ASR model due to their relevance as linguistic units. Furthermore, this strategy allowed us to keep track of different types of linguistic features characterizing the tokens. The analysis of the transcriptions generated by the model reveals that factors such as token frequency and lexical stress have a variable impact on the prediction strategies adopted by the ASR system.