The paper introduces a method for measuring accent differences that balances interpretability and practicality. It proposes using articulatory representations obtained via articulatory inversion as an interpretable basis for accent comparison, while employing optimal transport to compare accents across any type of recording. This approach aims to overcome the limitations of traditional phonetic analyses and embedding‑based methods, which are either time‑consuming or non‑interpretable.
arXiv:2604. 01562v2 Announce Type: replace-cross Abstract: Voice cloning is often evaluated in terms of overall quality, but less is known about accent preservation and its perceptual consequences.
arXiv:2603. 29042v2 Announce Type: replace-cross Abstract: Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive.
The paper introduces ABX-Accent, a benchmark built on the AESRC dataset that evaluates how well representation learning models adapt to 10 different English accents with less than 10 hours of unlabeled data per accent. It adapts the Zero Resources Challenge ABX metrics for each accent and demonstrates a baseline using adaptive domain normalization to fine‑tune a Contrastive Predictive Coding model, achieving a 23.6% relative improvement on across‑speaker ABX scores compared to non‑adapted models. The dataset and evaluation metrics will be released publicly after the paper is accepted.
arXiv:2608.30853v1 Announce Type: cross Abstract: While automatic speech recognition (ASR) models have achieved remarkable improvements in recent years, performance disparities persist across differe...
arXiv:2605. 03297v2 Announce Type: replace-cross Abstract: ASR systems based on self-supervised acoustic pretraining and CTC fine-tuning achieve strong performance on native speech but remain sensitive to accent variability.
The paper introduces an Encoding Probe that reconstructs language model representations using interpretable features, addressing limitations of traditional decoding probes such as incomparable feature contributions and correlation effects. It evaluates this approach on text and speech transformer models, examining features from acoustics, phonetics, syntax, lexicon, and speaker identity. Findings reveal that speaker-related effects vary with training objectives and datasets, while syntactic and lexical features independently contribute to reconstruction, offering a complementary perspective on model interpretation.
The paper investigates whether audio‑language models capture paralinguistic cues beyond spoken content. Using the Expresso dataset and four open‑source models, the authors trace how speaking style information is encoded in the late layers of the audio encoder but is degraded before reaching the final output. They find that some models are content‑driven while others are acoustic‑driven, revealing a gap between what is encoded and what is utilized in current audio‑language models.
The paper introduces a new evaluation framework for phonetic encoding algorithms, using a generalized Rand Index called the Hüllermeier‑Rifqi Index. It measures discordance by comparing pairwise similarity scores of ground‑truth IPA transcriptions with those of encoded strings, adjusted against a random generator. The method is applied to multilingual datasets, assessing recall via collision rate and demonstrating its use in evaluating orthographic transparency.
The paper introduces ABX-Accent, a benchmark for evaluating how well unsupervised speech representation learning models adapt to new accents. It uses the AESRC dataset with 10 English accents, each providing less than 10 hours of unlabeled data, and adapts the Zero Resources Challenge ABX metrics to these accents. A baseline model employing adaptive domain normalization to fine‑tune a Contrastive Predictive Coding model shows a 23.6% relative improvement on across‑speaker ABX scores compared to non‑adapted models.
arXiv:2608.21950v1 Announce Type: cross Abstract: Arabic automatic speech recognition (ASR) faces unique challenges due to diglossia, extensive regional dialect variation, and limited speech resource...
arXiv:2607. 23808v1 Announce Type: cross Abstract: In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India.
Neural TTS systems can sound natural across languages, but naturalness does not guarantee the preservation of sound contrasts that distinguish words from their grammatical forms. Standard metrics like MOS do not test for this.