Context-Aware ASR for Mandarin Technical Lectures

2026-07-06

AI Summary

This study addresses the challenge of evaluating automatic speech recognition (ASR) performance on Mandarin technical lectures, where critical English terms are frequently interspersed and conventional character error rate (CER) metrics fail to capture term-level accuracy. To overcome this limitation, the authors propose a reference-free, two-stage decoding approach: the first stage performs standard ASR and automatically extracts high-frequency technical terms from the output to construct a dynamic, context-aware lexicon; the second stage leverages this lexicon to guide rescoring and refine recognition results. This work introduces a novel term-centric evaluation metric that exposes blind spots in CER and demonstrates consistent improvements across five mainstream ASR systems. Experimental results show substantial gains in term recall (up to 62.05% with Breeze-ASR-25) and precision (82.73%), while maintaining or even reducing overall CER.

Abstract

Technical lectures mix Mandarin speech with English technical terms. These terms carry the core meaning of the lecture, yet they occupy few characters. Character error rate (CER) therefore hides their recognition failures. We study whether lecture context helps recognize these terms. We build a term-rich Mandarin AI/ML lecture benchmark, and we define term-centric metrics that measure technical-term recognition directly. We then propose a two-pass, reference-free decoding method. The first pass runs segment-only ASR. We extract the most frequent technical terms from the first-pass hypotheses, and we prompt the recognizer with this self-built glossary in the second pass. Across five ASR backbones, the first-pass glossary raises term recall for every model and holds or lowers CER on all five. On Breeze-ASR-25 it lifts term recall from 52.50% to 60.13% while lowering CER, and a hybrid that adds a small external term list reaches 62.05% recall and 82.73% term precision. Lecture context, recovered from the model's own output, is a practical signal for technical-term recognition. Term-centric evaluation exposes errors that CER misses.

Problem

• technical-term recognition
• Mandarin-English code-switching
• context-aware ASR
• term-centric evaluation
• automatic speech recognition

Innovation

• context-aware ASR
• technical term recognition
• two-pass decoding
• term-centric evaluation
• glossary prompting