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

arXiv: https://arxiv.org/abs/2506.06888 · HTML: https://mlsanity.com/paper/2506.06888/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 7 Jun 2025.

## Builds on (most cited first, 7 of 7)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network](https://mlsanity.com/paper/2305.12493/) (2023)
- [Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations](https://mlsanity.com/paper/1805.03322/) (2018)
- [Improving Contextual Recognition of Rare Words with an Alternate Spelling Prediction Model](https://mlsanity.com/paper/2209.01250/) (2022)
- [Self-supervised Speech Representations Still Struggle with African American Vernacular English](https://mlsanity.com/paper/2408.14262/) (2024)
- [Analysis of LLM as a grammatical feature tagger for African American English](https://mlsanity.com/paper/2502.06004/) (2025)

## Built on by (most cited first, 2 of 2)

- [Layer-wise Probing of wav2vec 2.0 and Whisper for Consonant Cluster Reduction in African American English](https://mlsanity.com/paper/2606.23948/) (2026)
- [NADI 2026: The Second Multidialectal Arabic Speech Processing Shared Task](https://mlsanity.com/paper/2609.27086/) (2026)

## Abstract (arXiv)

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.