Zero-Shot Speech LLMs for Multi-Aspect Evaluation of L2 Speech: Challenges and Opportunities

TL;DR

This paper evaluates the zero-shot capabilities of a speech LLM in assessing multiple aspects of L2 English pronunciation, highlighting its potential and current limitations for scalable language learning feedback.

Contribution

It introduces the first large-scale evaluation of a speech LLM for multi-aspect L2 speech assessment in a zero-shot setting, demonstrating promising results and identifying key challenges.

Findings

Strong agreement with human ratings for high-quality speech

Overprediction of scores for low-quality speech

Lacks precision in error detection

Abstract

An accurate assessment of L2 English pronunciation is crucial for language learning, as it provides personalized feedback and ensures a fair evaluation of individual progress. However, automated scoring remains challenging due to the complexity of sentence-level fluency, prosody, and completeness. This paper evaluates the zero-shot performance of Qwen2-Audio-7B-Instruct, an instruction-tuned speech-LLM, on 5,000 Speechocean762 utterances. The model generates rubric-aligned scores for accuracy, fluency, prosody, and completeness, showing strong agreement with human ratings within +-2 tolerance, especially for high-quality speech. However, it tends to overpredict low-quality speech scores and lacks precision in error detection. These findings demonstrate the strong potential of speech LLMs in scalable pronunciation assessment and suggest future improvements through enhanced prompting, calibration, and phonetic integration to advance Computer-Assisted Pronunciation Training.