Benchmarking Commercial ASR Systems on Code-Switching Speech: Arabic, Persian, and German

TL;DR

This paper introduces a comprehensive benchmark for evaluating commercial ASR systems on code-switching speech in Arabic, Persian, and German, highlighting performance differences and proposing more reliable evaluation metrics.

Contribution

It provides a new benchmark dataset and evaluation pipeline for multilingual code-switching ASR, including a cost-effective scoring method and analysis of semantic similarity metrics.

Findings

ElevenLabs Scribe v2 achieved the lowest WER (13.2%) across all language pairs.

BERTScore proved more reliable than WER for Arabic and Persian code-switching evaluation.

Difficulty-stratified analysis revealed performance gaps hidden in aggregate metrics.

Abstract

Code-switching -- the natural alternation between two languages within a single utterance -- represents one of the most challenging and under-studied conditions for automatic speech recognition (ASR). Existing commercial ASR benchmarks predominantly evaluate clean, monolingual audio and report a single Word Error Rate (WER) figure that tells practitioners little about real-world multilingual performance. We present a benchmark evaluating five commercial ASR providers across four language pairs: Egyptian Arabic--English, Saudi Arabic (Najdi/Hijazi)--English, Persian (Farsi)--English, and German--English. Each dataset comprises 300 samples selected by a two-stage pipeline: a heuristic filter scoring transcripts on five structural code-switching signals, followed by a GPT-4o and Gemini 1.5 Pro ensemble scoring candidates across six linguistic dimensions. This pipeline reduces LLM scoring costs by approximately 91% relative to exhaustive scoring. We evaluate the systems on both WER and BERTScore, arguing that BERTScore is a more reliable metric for Arabic and Persian pairs where transliteration variance causes WER to penalise semantically correct transcriptions. ElevenLabs Scribe v2 achieves the lowest WER across all four language pairs (13.2% overall; 13.1% on Egyptian Arabic) and leads on BERTScore (0.936 overall). We further demonstrate that difficulty-stratified analysis reveals performance gaps masked by aggregate averages, and that BERT embedding projections confirm semantic proximity between reference and hypothesis despite surface-level script differences. The benchmarking dataset is publicly available at https://huggingface.co/datasets/Perle-ai/ASR_Code_Switch.