Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data

• URL: http://arxiv.org/abs/2603.07534v1
• Date: Sun, 08 Mar 2026 08:48:42 GMT
• Title: Accent Vector: Controllable Accent Manipulation for Multilingual TTS Without Accented Data
• Abstract summary: We propose textitAccent Vector, a controllable representation that enables accent manipulation in multilingual TTS.<n>textitAccent Vector is derived by fine-tuning a TTS system on native speech of a different language.<n>By scaling and interpolating the vector, we achieve fine-grained control over accent strength and generate mixed-accent speech.
• Score: 45.10765052993173
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity. The majority of English speakers are non-native (L2) speakers, yet current Text-To-Speech (TTS) systems primarily model American-accented English due limited accented data. We propose \textit{Accent Vector}, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data. \textit{Accent Vector} is derived by fine-tuning a TTS system on native speech of a different language (i.e. non-English) and computing task vectors capturing accent characteristics (i.e. in English). By scaling and interpolating the vector, we achieve fine-grained control over accent strength and generate mixed-accent speech. In addition, it generalizes beyond English, enabling accent control across multiple languages. Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.

Related papers

• CrossAccent-TTS: Cross-Lingual Accent-Intensity Controllable Text-to-Speech via Disentangled Speaker and Accent Representations [9.312579603351827]
  CrossAccentTTS is a framework that enables accent control and conversion while preserving speaker identity.<n>Experiments on the Indic Multilingual and L2-arctic datasets show that CrossAccent-TTS achieves precise control of accent intensity.
  arXiv  Detail & Related papers  (2026-06-24T05:02:36Z)
• Learning-free L2-Accented Speech Generation using Phonological Rules [46.06717601946553]
  We propose a accented text-to-speech framework that combines phonological rules with a multilingual TTS model.<n>The rules are applied to phoneme sequences to transform accent at the phoneme level while preserving intelligibility.<n>We design rule sets for Spanish- and Indian-accented English, modeling systematic differences in consonants, vowels, and syllable structure arising from phonotactic constraints.
  arXiv  Detail & Related papers  (2026-03-08T09:22:55Z)
• Accent conversion using discrete units with parallel data synthesized from controllable accented TTS [56.18382038512251]
  The goal of accent conversion (AC) is to convert speech accents while preserving content and speaker identity. Previous methods either required reference utterances during inference, did not preserve speaker identity well, or used one-to-one systems that could only be trained for each non-native accent. This paper presents a promising AC model that can convert many accents into native to overcome these issues.
  arXiv  Detail & Related papers  (2024-09-30T19:52:10Z)
• Accent Conversion in Text-To-Speech Using Multi-Level VAE and Adversarial Training [14.323313455208183]
  Inclusive speech technology aims to erase any biases towards specific groups, such as people of certain accent. We propose a TTS model that utilizes a Multi-Level Variational Autoencoder with adversarial learning to address accented speech synthesis and conversion.
  arXiv  Detail & Related papers  (2024-06-03T05:56:02Z)
• Accented Speech Recognition With Accent-specific Codebooks [53.288874858671576]
  Speech accents pose a significant challenge to state-of-the-art automatic speech recognition (ASR) systems. Degradation in performance across underrepresented accents is a severe deterrent to the inclusive adoption of ASR. We propose a novel accent adaptation approach for end-to-end ASR systems using cross-attention with a trainable set of codebooks.
  arXiv  Detail & Related papers  (2023-10-24T16:10:58Z)
• Multilingual Multiaccented Multispeaker TTS with RADTTS [21.234787964238645]
  We present a multilingual, multiaccented, multispeaker speech synthesis model based on RADTTS. We demonstrate an ability to control synthesized accent for any speaker in an open-source dataset comprising of 7 accents.
  arXiv  Detail & Related papers  (2023-01-24T22:39:04Z)
• Explicit Intensity Control for Accented Text-to-speech [65.35831577398174]
  How to control the intensity of accent in the process of TTS is a very interesting research direction. Recent work design a speaker-versaadrial loss to disentangle the speaker and accent information, and then adjust the loss weight to control the accent intensity. This paper propose a new intuitive and explicit accent intensity control scheme for accented TTS.
  arXiv  Detail & Related papers  (2022-10-27T12:23:41Z)
• Controllable Accented Text-to-Speech Synthesis [76.80549143755242]
  We propose a neural TTS architecture that allows us to control the accent and its intensity during inference. This is the first study of accented TTS synthesis with explicit intensity control.
  arXiv  Detail & Related papers  (2022-09-22T06:13:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.