Cascaded vs. End-to-End Speech Translation Across 80+ Languages: Accuracy, Latency, and the Long-Tail Problem in Frontline Customer Service
DOI:
https://doi.org/10.15680/IJCTECE.2022.0504008Keywords:
speech translation, cascaded pipeline, end to end translation, multilingual natural language processing, automatic speech recognition, low resource languages, contact center automation, latency, customer experienceAbstract
Frontline customer service operations increasingly rely on automated speech translation to connect support agents and self service voice bots with callers across dozens of languages. This article compares two dominant system designs, the cascaded pipeline that chains automatic speech recognition with machine translation, and the end to end model that maps source speech directly to target text or speech, across an evaluation set of 82 languages spanning high resource, mid resource, low resource, and long tail categories. Drawing on publicly documented benchmark methodology and operational telemetry patterns reported in the research literature through mid 2022, the analysis finds that cascaded systems retain an accuracy advantage in high resource languages and a notable robustness advantage in long tail languages where parallel speech to text corpora are scarce, while end to end systems offer materially lower latency and smaller error compounding in mid resource conditions. The long tail problem, defined here as the systematic accuracy and reliability gap experienced by the languages that fall outside the top 20 most resourced languages, is shown to be less a modeling problem and more a data availability problem, with direct implications for how enterprises should prioritize localization investment. The article closes with a decision framework for contact center and customer experience teams choosing between architectures, and a set of hybrid design patterns that borrow strengths from both approaches.
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