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

Thanathai Lertpetchpun, Thanapat Trachu, Jihwan Lee, Tiantian Feng, Dani Byrd, Shrikanth Narayanan · Mar 8, 2026 · Citations: 0

Human Eval
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Data freshness

Extraction: Fresh

Check recency before relying on this page for active eval decisions. Use stale pages as context and verify against current hub results.

Metadata refreshed

Mar 8, 2026, 8:48 AM

Recent

Extraction refreshed

Mar 14, 2026, 1:30 AM

Fresh

Extraction source

Persisted extraction

Confidence 0.30

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.

Low-signal caution for protocol decisions

Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.

  • Extraction flags indicate low-signal or possible false-positive protocol mapping.
  • Extraction confidence is 0.30 (below strong-reference threshold).
  • No benchmark/dataset or metric anchors were extracted.
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HFEPX Relevance Assessment

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

Extraction flags indicate low-signal or possible false-positive protocol mapping.

Trust level

Low

Eval-Fit Score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

HFEPX Fit

Adjacent candidate

Extraction confidence: Low

If you are doing eval pipeline work, start here:

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Field Provenance & Confidence

Each key protocol field shows extraction state, confidence band, and data source so you can decide whether to trust it directly or validate from full text.

Human Feedback Types

missing

None explicit

Confidence: Low Source: Persisted extraction missing

No explicit feedback protocol extracted.

Evidence snippet: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity.

Evaluation Modes

partial

Human Eval

Confidence: Low Source: Persisted extraction evidenced

Includes extracted eval setup.

Evidence snippet: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity.

Quality Controls

missing

Not reported

Confidence: Low Source: Persisted extraction missing

No explicit QC controls found.

Evidence snippet: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity.

Benchmarks / Datasets

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No benchmark anchors detected.

Evidence snippet: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity.

Reported Metrics

missing

Not extracted

Confidence: Low Source: Persisted extraction missing

No metric anchors detected.

Evidence snippet: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity.

Rater Population

missing

Unknown

Confidence: Low Source: Persisted extraction missing

Rater source not explicitly reported.

Evidence snippet: Accent is an integral part of society, reflecting multiculturalism and shaping how individuals express identity.

Human Data Lens

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Unknown
  • Unit of annotation: Unknown
  • Expertise required: Multilingual
  • Extraction source: Persisted extraction

Evaluation Lens

  • Evaluation modes: Human Eval
  • Agentic eval: None
  • Quality controls: Not reported
  • Confidence: 0.30
  • Flags: low_signal, possible_false_positive

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Deterministic synthesis

We propose Accent Vector, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data. HFEPX signals include Human Eval with confidence 0.30. Updated from current HFEPX corpus.

Generated Mar 14, 2026, 1:30 AM · Grounded in abstract + metadata only

Key Takeaways

  • We propose Accent Vector, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data.
  • Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.

Researcher Actions

  • Treat this as method context, then pivot to protocol-specific HFEPX hubs.
  • Identify benchmark choices from full text before operationalizing conclusions.
  • Verify metric definitions before comparing against your eval pipeline.

Caveats

  • Generated from title, abstract, and extracted metadata only; full-paper implementation details are not parsed.
  • Low-signal flag detected: protocol relevance may be indirect.

Research Summary

Contribution Summary

  • We propose Accent Vector, a controllable representation that enables accent manipulation in multilingual TTS without requiring accented training data.
  • Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.

Why It Matters For Eval

  • Objective and human evaluations confirm the effectiveness of Accent Vector for fine-grained and compositional accent control.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Human Eval

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Gap: Metric reporting is present

    No metric terms extracted.

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