ASR bias causes users from underrepresented dialects to internalize failures as personal inadequacy and perform extensive emotional and linguistic labor, revealing harms missed by accuracy-only evaluations.
Quantifying bias in automatic speech recogni- tion
8 Pith papers cite this work, alongside 53 external citations. Polarity classification is still indexing.
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ST models override masculine ILM biases with acoustic input, using first-person pronouns to link terms to the speaker and accessing gender cues across the full frequency spectrum rather than pitch alone.
The authors perform the first systematic bias evaluation in speech continuation tasks across three models, revealing gender interactions in text metrics and stronger reversion to modal phonation for female prompts.
Vaani Benchmark V1.0 is a multimodal Hindi ASR dataset from 104 districts featuring spontaneous speech recordings in real-world conditions and three independent transcriptions per segment for robust multi-reference evaluation.
Evaluation of WhisperIPA and ZIPA reveals persistent performance gaps across languages, accents, gender, ethnicity, and age even after allowing for similar phoneme substitutions.
Random phoneme substitutions recover most ASR gains from synthetic accented speech, with targeted edits and ground-truth prosody providing only marginal additional benefits.
Omnimodal models show reduced demographic bias in image and video tasks compared to substantial biases and lower performance in audio tasks.
citing papers explorer
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"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias
ASR bias causes users from underrepresented dialects to internalize failures as personal inadequacy and perform extensive emotional and linguistic labor, revealing harms missed by accuracy-only evaluations.
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Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation
ST models override masculine ILM biases with acoustic input, using first-person pronouns to link terms to the speaker and accessing gender cues across the full frequency spectrum rather than pitch alone.
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Speak Your Mind: The Speech Continuation Task as a Probe of Voice-Based Model Bias
The authors perform the first systematic bias evaluation in speech continuation tasks across three models, revealing gender interactions in text metrics and stronger reversion to modal phonation for female prompts.
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Vaani Benchmark V1.0: An Inclusive Multimodal Benchmark Dataset for Hindi
Vaani Benchmark V1.0 is a multimodal Hindi ASR dataset from 104 districts featuring spontaneous speech recordings in real-world conditions and three independent transcriptions per segment for robust multi-reference evaluation.
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Evaluating Bias in Phoneme-Based Automatic Speech Recognition Systems: An Analysis of IPA Transcription Models
Evaluation of WhisperIPA and ZIPA reveals persistent performance gaps across languages, accents, gender, ethnicity, and age even after allowing for similar phoneme substitutions.
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Few-Shot Synthetic Accented Speech for ASR Fine-Tuning: What Helps and When?
Random phoneme substitutions recover most ASR gains from synthetic accented speech, with targeted edits and ground-truth prosody providing only marginal additional benefits.
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Demographic and Linguistic Bias Evaluation in Omnimodal Language Models
Omnimodal models show reduced demographic bias in image and video tasks compared to substantial biases and lower performance in audio tasks.
- Toward Fair Speech Technologies: A Comprehensive Survey of Bias and Fairness in Speech AI