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arxiv: 2606.19792 · v1 · pith:E7FLUMVGnew · submitted 2026-06-18 · 💻 cs.SD

Exploring Pre-training Benefits on Phoneme Addition through Fine-tuning in Speech Synthesis

Masato Murata , Koichi Miyazaki , Tomoki Koriyama , Tomoki Toda This is my paper

Pith reviewed 2026-06-26 16:18 UTC · model grok-4.3

classification 💻 cs.SD
keywords phoneme additiontext-to-speech synthesisfine-tuningpre-trainingtransfer learningnaturalnessphoneme error ratecross-lingual
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The pith

Pre-training improves naturalness in TTS fine-tuning but offers limited benefit for learning new phonemes.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether pre-trained text-to-speech models help when the target corpus introduces new phonemes unseen during pre-training, a process called phoneme addition. It tests this in a controlled simulation using LLM-generated phoneme-controlled corpora and in a real English-to-Japanese cross-lingual transfer setting. Results indicate that fine-tuning a pre-trained model produces higher naturalness than training from scratch, yet requires as much or more target data to reach comparable phoneme error rates on the new phonemes. This pattern holds in both setups and points to pre-training mainly aiding general speech quality rather than the specific task of incorporating unfamiliar phonemes.

Core claim

In both the simulation and real-speech settings, fine-tuning a pre-trained model achieved higher naturalness scores than training from scratch, but required as much or more data to achieve comparable phoneme error rates for the new phonemes. These findings indicate that pre-training contributes primarily to improvements in naturalness while providing limited assistance for the process of phoneme addition.

What carries the argument

The direct comparison of data efficiency for phoneme error rate on unseen phonemes between fine-tuned pre-trained models and models trained from scratch, using phoneme-controlled corpora in simulation and cross-lingual real data.

If this is right

  • Fine-tuning always yields higher naturalness than scratch training even when adding phonemes.
  • Comparable phoneme accuracy on new sounds takes the same or greater amount of fine-tuning data whether starting from a pre-trained model or not.
  • Pre-training benefits are concentrated in aspects that affect perceived naturalness rather than in acquiring new phoneme production.
  • The process of expanding the phoneme inventory during fine-tuning does not leverage prior knowledge of other phonemes effectively.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the goal is only to add new phonemes with minimal data, training from scratch may be equally efficient.
  • Models might benefit from targeted adaptation methods focused on the new phoneme embeddings rather than full fine-tuning.
  • These results could inform choices in multilingual TTS systems where phoneme overlap varies across languages.

Load-bearing premise

The LLM-generated phoneme-controlled corpora in the simulation setup produce phoneme addition behavior that matches what occurs with actual human speech recordings.

What would settle it

An experiment showing that pre-trained models reach the target phoneme error rate on new phonemes with substantially less fine-tuning data than scratch-trained models in the real-speech setting.

Figures

Figures reproduced from arXiv: 2606.19792 by Koichi Miyazaki, Masato Murata, Tomoki Koriyama, Tomoki Toda.

Figure 1
Figure 1. Figure 1: Overview of the phoneme addition process in trans￾fer learning. The pre-trained model is trained on a source dataset. During fine-tuning, the phoneme inventory is expanded by adding target phonemes (red). ments in overall naturalness and intelligibility, leaving it unclear whether pre-trained phoneme knowledge really contributes to the phoneme addition process itself. To verify the benefits of pre-trained … view at source ↗
Figure 3
Figure 3. Figure 3: (a) Target PER (%) on Japanese-specific target phonemes and (b) UTMOS score in a real-speech cross-lingual transfer learning setting (English VCTK → Japanese JSUT), comparing fine-tuning (blue solid) and scratch training (red dashed) across various dataset sizes. to acquire newly added phonemes. 4.2. Results in Real-speech Cross-lingual Transfer Setting [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

Transfer learning is widely used for low-resource text-to-speech. When the target corpus contains phonemes unseen in pre-training, the model must expand its phoneme inventory during fine-tuning; we call the process "phoneme addition." However, it remains unclear whether the pre-trained ability to generate seen phonemes contributes to this process. This study investigates phoneme addition in two settings: (1) a simulation setup using LLM-generated phoneme-controlled corpora that enables investigation without considering confounding factors, and (2) a real-speech cross-lingual transfer setup (English to Japanese) to validate whether the findings hold in practice. Experiments in both settings showed that while fine-tuning achieved higher naturalness than training from scratch, it required as much or more data to achieve comparable PER for new phonemes. These results indicate that pre-training mainly contributes to naturalness improvement, but offers limited benefit for phoneme addition.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims that pre-training in TTS mainly improves naturalness during fine-tuning but offers limited benefit for phoneme addition: in both an LLM-generated phoneme-controlled simulation and an English-to-Japanese real-speech transfer, fine-tuning requires as much or more data than training from scratch to reach comparable phoneme error rates (PER) on unseen phonemes.

Significance. If the results hold, the work indicates that pre-training contributes primarily to prosodic and acoustic naturalness rather than efficient expansion of the phoneme inventory, which could inform the design of future pre-training objectives or targeted data collection for low-resource phoneme-addition scenarios. The use of complementary simulation and real-speech setups is a methodological strength.

major comments (1)
  1. [Simulation setup (abstract and methods)] Simulation setup: the central claim that pre-training offers limited benefit for phoneme addition rests on the LLM-generated corpora producing representative phoneme-learning dynamics. The abstract states the simulation “enables investigation without considering confounding factors,” but provides no evidence (e.g., statistical comparison of phoneme-context distributions, coarticulation patterns, or duration variability) that the generated data match real speech; if the simulation distorts acquisition behavior, the data-efficiency result does not generalize to the English-to-Japanese case.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the single major comment below and will revise the manuscript to incorporate additional validation where appropriate.

read point-by-point responses

  1. Referee: Simulation setup: the central claim that pre-training offers limited benefit for phoneme addition rests on the LLM-generated corpora producing representative phoneme-learning dynamics. The abstract states the simulation “enables investigation without considering confounding factors,” but provides no evidence (e.g., statistical comparison of phoneme-context distributions, coarticulation patterns, or duration variability) that the generated data match real speech; if the simulation distorts acquisition behavior, the data-efficiency result does not generalize to the English-to-Japanese case.

    Authors: We acknowledge that the original manuscript does not include direct statistical comparisons demonstrating that the LLM-generated corpora match real-speech distributions in phoneme contexts, coarticulation, or duration. The simulation was intentionally constructed to isolate phoneme-addition dynamics by removing real-speech confounders (e.g., speaker variability, recording conditions), which is its stated purpose. Nevertheless, to strengthen the claim of representativeness and to address generalizability, the revised version will add quantitative comparisons of phoneme-context distributions, coarticulation statistics, and duration variability between the simulated data and the real English-to-Japanese corpora. These additions will clarify the degree to which the simulation reproduces acquisition behavior and will explicitly link the simulation results to the real-speech transfer findings. revision: yes

Circularity Check

0 steps flagged

Purely empirical study; no derivations or fitted predictions

full rationale

The paper reports experimental comparisons of fine-tuning vs. scratch training on phoneme addition in two setups (LLM-simulated corpora and English-to-Japanese real speech). No equations, parameter fits, uniqueness theorems, or ansatzes are presented as predictions. Conclusions are direct measurements of naturalness and PER; no step reduces by construction to its own inputs or self-citations. This is the expected non-finding for an empirical measurement study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical axioms, free parameters, or invented entities are introduced; the work is an empirical comparison relying on standard supervised fine-tuning assumptions.

pith-pipeline@v0.9.1-grok · 5689 in / 1103 out tokens · 25096 ms · 2026-06-26T16:18:17.220824+00:00 · methodology

discussion (0)

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Reference graph

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28 extracted references · 4 canonical work pages · 2 internal anchors

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    Introduction Recent text-to-speech (TTS) systems have achieved human- level naturalness in high-resource scenarios. However, develop- ing low-resource language TTS remains challenging. To solve this data limitation issue, transfer learning is a standard ap- proach, where a model is pre-trained on a high-resource lan- guage and subsequently fine-tuned on t...

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    Experiments This study focuses on two questions: whether a model can acquire target phonemes unseen in pre-training through fine- tuning, and how pre-trained knowledge benefits this process. We conducted two experiments under simulated phoneme- controlled settings and real-speech cross-lingual transfer set- tings. In the simulated phoneme-controlled setti...

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    Experimental Results 4.1. Results in Simulated Phoneme-Controlled Setting Figure 2 shows Target PER and UTMOS score comparisons be- tween fine-tuning and training from scratch for plosive conso- nants and front vowels across various dataset sizes (100, 300, 500, 800, 1,000, and 2,000). Across both target phoneme types, training from scratch achieved compa...

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    Conclusions This study investigated how pre-trained phoneme knowledge contributes to the phoneme addition process through fine- tuning. We conducted experiments under two settings: (1) a phoneme-controlled simulation using LLM-generated corpora to enable the comprehensive evaluation without considering confounding factors, and (2) a real-speech cross-ling...

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    Acknowledgments This work was supported in part by JSPS KAKENHI Grant Number 26H02530, and in part by the BRIDGE Program (R7-H05), implemented by the Cabinet Office, Government of Japan

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