Automatic Speech Recognition (ASR) transcription accuracy remains highly sensitive to audio segmentation strategies, yet most benchmarks assume oracle timestamps unavailable in deployment. We systematically evaluate how audio segmentation affects Whisper’s performance on 10 hours of Persian YouTube content, comparing transcript-aligned (oracle) versus silence-based (realistic) approaches across contrasting acoustic conditions. Results reveal striking content-type dependency: podcast content benefits from timestamp segmentation (33% lower mean WER), while entertainment content favors silence-based segmentation (8% lower mean WER). This finding demonstrates that optimal segmentation must be content-aware, with silence detection better capturing natural boundaries in acoustically heterogeneous media while avoiding mid-utterance splits. We publicly release our evaluation framework, 10 hours of audio with gold transcripts, and segmentation results here: https://github.com/ri164-bolleit/persian-youtube-whisper-benchmark

In this paper we discuss the design and implementation of a parser for German and Arabic, which is currently being used in a tutoring system for foreign language training. Computer-aided language tutoring is a good application for testing the robustness and flexibility of a parsing system, since the input is usually ungrammatical in some way. Efficiency is also a concern, as tutoring applications typically run on personal computers, with the parser sharing memory with other components of the system. Our system is principle-based, which ensures a compact representation, and improves portability, needed in order to extend the initial design from German to Arabic and (eventually) Spanish. Currently, the parser diagnoses agreement errors, case errors, selection errors, and some word order errors. The parser can handle simple and complex declaratives and questions, topicalisations, verb movement, relative clauses — broad enough coverage to be useful in the design of real exercises and dialogues.