Adversarial Audio Perturbations Cause Transcription Errors and Identity Drift in ASR and Speaker Verification

Global: Adversarial Audio Perturbations Cause Transcription Errors and Identity Drift in ASR and Speaker Verification

A recent study published on arXiv in September 2025 examines how adversarial perturbations in speech affect automatic speech recognition (ASR) and speaker verification systems. The paper, authored by a team of researchers, investigates phonetic‑level distortions and reports that such modifications can lead to transcription errors and shifts in speaker identity. Using DeepSpeech as the ASR target, the authors generated targeted adversarial examples and evaluated their impact on speaker embeddings.

Phonetic Patterns in Adversarial Audio

The researchers focused on phonetic confusions, noting systematic patterns such as vowel centralization and consonant substitution. These findings suggest that adversarial audio exploits predictable phonetic weaknesses, which can be quantified across a set of test phrases.

Impact on Speaker Verification

To assess the effect on speaker verification, the study measured identity drift by comparing speaker embeddings from genuine and adversarial samples. The results indicated a measurable degradation in speaker‑specific cues, potentially compromising verification accuracy.

Experimental Methodology

The experimental design included 16 phonetically diverse target phrases. Each phrase was subjected to targeted attacks designed to force specific transcription outcomes while remaining imperceptible to human listeners.

Results on Transcription and Identity Drift

Evaluation of DeepSpeech revealed that the adversarial examples produced transcription errors consistent with the intended target phrases. Concurrently, speaker embedding analysis showed shifts that could be interpreted as identity drift.

Implications for Defense Strategies

The authors argue that current defenses, which often focus on signal‑level perturbations, may be insufficient because they do not address the underlying phonetic manipulation. They recommend developing phonetic‑aware defense mechanisms.

Broader Security Considerations

The study underscores the broader security implications for voice‑activated services, suggesting that both ASR and speaker verification components require joint robustness assessments.

Future Research Directions

Future work outlined by the authors includes extending the analysis to other ASR architectures and exploring mitigation strategies that incorporate phonetic consistency checks.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.