arXiv AI
Controllable Accent Normalization via Discrete Diffusion
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arXiv AI
Jul 7
TokAN: Accent Normalization Using Self-Supervised Speech Tokens
arXiv:2607. 03928v1 Announce Type: cross Abstract: Accent normalization (AN) seeks to convert non-native (L2) accented speech into standard (L1) speech while preserving speaker identity.
arXiv Machine Learning
Jun 16
Contrastive Regularization for Accent-Robust ASR
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.
arXiv Machine Learning
Sep 24
PHONOS: PHOnetic Neutralization for Online Streaming Applications
PHONOS is a real‑time streaming module for speaker anonymization that neutralizes accent cues by converting non‑native segmental realizations toward a target accent domain. It uses pre‑generated golden utterances that preserve timbre and rhythm, aligning them with silence‑aware DTW and applying zero‑shot voice conversion to supervise a causal accent translator. The system achieves an 81% reduction in non‑native accent confidence, improves accentedness ratings, reduces speaker linkability in embedding space, and operates with ≤241 ms end‑to‑end latency on a single GPU.
arXiv AI
Sep 12
Flexible and Interpretable Accent Distance Measurements
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 Computation and Language
Sep 4
Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition
Dual-Form ASR (DF-ASR) is a framework that unifies spoken-form ASR and semantics-aware written-form inverse text normalization (ITN) for Chinese speech recognition. It uses paired spoken- and written-form supervision generated and judged by a large language model, and introduces an ITN-MWER objective to penalize errors on normalization-sensitive spans. DF-ASR also employs a REQUIRE-ITN/FORBID-ITN protocol to separately evaluate required normalization and forbidden-span preservation, achieving superior performance over open-source ASR-ITN systems while maintaining prompt-level control between transcript forms.
arXiv AI
Oct 12
AudioTETRIS: Token Elimination via Transitional Importance Signals for Efficient Audio LLMs
AudioTETRIS is a training‑free, two‑stage token elimination framework designed for large audio language models. It leverages the geometry of audio encoder representations as an acoustic prior to compute per‑token importance scores, which guide progressive top‑k token selection across decoder layers. Experiments on Qwen2‑Audio‑7B, Phi‑4‑Multimodal, and Audio Flamingo 3 show that AudioTETRIS retains near‑full performance while using only 25% of audio tokens and outperforms existing training‑free baselines.