SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models

TL;DR

SpeechIQ introduces a novel evaluation framework inspired by human cognition to assess voice understanding in large language models across multiple cognitive levels, surpassing traditional metrics.

Contribution

It proposes the first cognition-inspired evaluation pipeline for voice understanding LLMs, covering multiple cognitive levels and enabling comprehensive performance analysis.

Findings

SIQ quantifies voice understanding abilities effectively.

It enables comparison of different voice understanding models.

SIQ uncovers annotation errors and hallucinations in benchmarks.

Abstract

We introduce Speech-based Intelligence Quotient (SIQ) as a new form of human cognition-inspired evaluation pipeline for voice understanding large language models, LLM Voice, designed to assess their voice understanding ability. Moving beyond popular voice understanding metrics such as word error rate (WER), SIQ examines LLM Voice across three cognitive levels motivated by Bloom's Taxonomy: (1) Remembering (i.e., WER for verbatim accuracy); (2) Understanding (i.e., similarity of LLM's interpretations); and (3) Application (i.e., QA accuracy for simulating downstream tasks). We demonstrate that SIQ not only quantifies voice understanding abilities but also provides unified comparisons between cascaded methods (e.g., ASR LLM) and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM Voice. Our framework represents a first-of-its-kind intelligence examination that bridges cognitive principles with voice-oriented benchmarks, while exposing overlooked challenges in multi-modal training. Our code and data will be open source to encourage future studies.