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Automatic Speech Recognition of African American English: Lexical and Contextual Effects

Domaine:

natural language processing

Type de record:

paper
Créateur:
MojTan
Hôte:avatar
Automatic Speech Recognition (ASR) models often struggle with the phonetic, phonological, and morphosyntactic features found in African American English (AAE). This study focuses on two key AAE variables: Consonant Cluster Reduction (CCR) and ING-reduction. It examines whether the presence of CCR and ING-reduction increases ASR misrecognition. Subsequently, it investigates whether end-to-end ASR systems without an external Language Model (LM) are more influenced by lexical neighborhood effect and less by contextual predictability compared to systems with an LM. The Corpus of Regional African American Language (CORAAL) was transcribed using wav2vec 2.0 with and without an LM. CCR and ING-reduction were detected using the Montreal Forced Aligner (MFA) with pronunciation expansion. The analysis reveals a small but significant effect of CCR and ING on Word Error Rate (WER) and indicates a stronger presence of lexical neighborhood effect in ASR systems without LMs. submitted to Interspeech 2025

Visit

arxiv.org

Tasks

automatic speech recognitionspeech processing

Tags

Computation and LanguageSoundAudio and Speech ProcessingI.5; G.3

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