arXiv subjects

Shuhei Kato

Publications and source records attributed to Shuhei Kato.

5 recordsLinked to original sources

arXiv2026

A Geometry-Limited Identification Floor and Its Consequences for Voice-Clone Attribution in Professional Voice Actors

A voice actor's voice is their asset, and AI cloning directly threatens it. The natural defense flags the enrolled actor whose embedding similarity to a suspect recording crosses a threshold. We show it fails where it is most needed: trained voices crowd the embedding space, and each actor performs many styles. On 1,168 Japanese voice actors (56,568 segments, ~63 h), a misidentification floor survives calibration, score normalization, and discriminative re-ranking (linear and nonlinear, including PLDA): the residual is a limit of the embedding geometry, not of the back-ends we evaluate. The best ensemble still leaves ~2.6% closed-set misidentification, several-fold above matched controls; session-disjoint, re-ranking lowers the floor only to 13.0%. The same crowding drives false attribution: on a generic English encoder, roughly half the clones of non-enrolled people falsely accuse an enrolled actor, while -- by a separate real-vs-synthetic shift -- 32% of Seed-VC clones of enrolled targets are missed at the same threshold; one operating point couples the two, and none escapes both. A domain-matched, voice-actor-trained encoder mitigates substantially (a four-fold gender gap vanishes; wrongful misattribution falls to 1.5-10%), but does not remove the floor. Controls (codec, channel, vocoder, content) support reading the miss rate as a real-versus-synthetic covariate shift, not missing speaker information. Fixed-threshold clone attribution is thus unreliable here, and on a generic encoder unfair. Robust attribution must extend spoofing-aware speaker verification to open-set 1:N (anti-spoofing gate, domain-matched encoder, per-speaker calibration, abstain option), and even then supports detection, not autonomous enforcement.

eess.AS↗

arXiv2025

UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech

We propose UtterTune, a lightweight method for adapting a multilingual text-to-speech (TTS) system built on a large language model (LLM). It improves control of pronunciation in the target language while preserving performance in the others. Although LLM architectures have enabled TTS models to achieve remarkable naturalness, accurately modeling grapheme-to-phoneme (G2P) mapping and prosody remains challenging, especially when the model omits an explicit G2P module and directly processes minimally encoded text (e.g., byte-pair encoding). UtterTune leverages low-rank adaptation to enable the control of segmental pronunciation and pitch accent at the phoneme level for Japanese speech, the target language in this paper, while maintaining naturalness and speaker similarity in a zero-shot setting. Objective and subjective evaluations confirm its effectiveness.

cs.SD↗

arXiv2020

How Similar or Different Is Rakugo Speech Synthesizer to Professional Performers?

We have been working on speech synthesis for rakugo (a traditional Japanese form of verbal entertainment similar to one-person stand-up comedy) toward speech synthesis that authentically entertains audiences. In this paper, we propose a novel evaluation methodology using synthesized rakugo speech and real rakugo speech uttered by professional performers of three different ranks. The naturalness of the synthesized speech was comparable to that of the human speech, but the synthesized speech entertained listeners less than the performers of any rank. However, we obtained some interesting insights into challenges to be solved in order to achieve a truly entertaining rakugo synthesizer. For example, naturalness was not the most important factor, even though it has generally been emphasized as the most important point to be evaluated in the conventional speech synthesis field. More important factors were the understandability of the content and distinguishability of the characters in the rakugo story, both of which the synthesized rakugo speech was relatively inferior at as compared with the professional performers. We also found that fundamental frequency fo modeling should be further improved to better entertain audiences. These results show important steps to reaching authentically entertaining speech synthesis.

eess.AS↗

arXiv2020

Modeling of Rakugo Speech and Its Limitations: Toward Speech Synthesis That Entertains Audiences

We have been investigating rakugo speech synthesis as a challenging example of speech synthesis that entertains audiences. Rakugo is a traditional Japanese form of verbal entertainment similar to a combination of one-person stand-up comedy and comic storytelling and is popular even today. In rakugo, a performer plays multiple characters, and conversations or dialogues between the characters make the story progress. To investigate how close the quality of synthesized rakugo speech can approach that of professionals' speech, we modeled rakugo speech using Tacotron 2, a state-of-the-art speech synthesis system that can produce speech that sounds as natural as human speech albeit under limited conditions, and an enhanced version of it with self-attention to better consider long-term dependencies. We also used global style tokens and manually labeled context features to enrich speaking styles. Through a listening test, we measured not only naturalness but also distinguishability of characters, understandability of the content, and the degree of entertainment. Although we found that the speech synthesis models could not yet reach the professional level, the results of the listening test provided interesting insights: 1) we should not focus only on the naturalness of synthesized speech but also the distinguishability of characters and the understandability of the content to further entertain audiences; 2) the fundamental frequency (fo) expressions of synthesized speech are poorer than those of human speech, and more entertaining speech should have richer fo expression. Although there is room for improvement, we believe this is an important stepping stone toward achieving entertaining speech synthesis at the professional level.

eess.AS↗

arXiv2010

Experimental Determination of Electron Transition Probabilities in Elementary Electron-Phonon Scattering Processes Using Electron-Energy-Loss Spectroscopy: The Example of Graphite

We have investigated coupling constants in elementary electron-phonon scattering processes on a graphite surface by the combined use of high-resolution electron-energy-loss spectroscopy (HREELS) and very low-energy electron diffraction (VLEED). HREELS is used to measure the modulations of electron transition probabilities from incoming electrons in vacuum to outgoing electrons in vacuum where the transition includes one-phonon scattering processes inside a solid. Determining the electronic band structures of graphite with VLEED, we defined electronic states of the solid surface that electrons entered before and after scattering off phonons. Thus, we observed that the measured electron transition probabilities significantly depended on whether the electrons were in a bulk Bloch state or an evanescent state before scattering off the phonons. This result clearly indicates that the measured electron transition probabilities reflect the strength of the coupling constants in the solid.

cond-mat.mtrl-sci↗