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20212026
most citedTTSDS -- Text-to-Speech Distribution Score

1 citations · 1 across the 9 of their papers we have counts for

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eess.AS2026

An Evaluation Framework for Text-to-Speech Voice Reconstruction

Ariadna Sanchez, Christoph Minixhofer, Korin Richmond +3

Voice reconstruction using Text-to-Speech (TTS) offers a communication method for people with speech disorders, which aims to retain their speaker identity while improving intellig…

eess.AS2026

The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs

Shree Harsha Bokkahalli Satish, Christoph Minixhofer, Maria Teleki +5

Speech Large Language Models (SpeechLLMs) process spoken input directly, retaining cues such as accent and perceived gender that were previously removed in cascaded pipelines. This…

eess.AS2024

Beyond Oversmoothing: Evaluating DDPM and MSE for Scalable Speech Synthesis in ASR

Christoph Minixhofer, Ondrej Klejch, Peter Bell

Synthetically generated speech has rapidly approached human levels of naturalness. However, the paradox remains that ASR systems, when trained on TTS output that is judged as natur…

eess.AS2024★ 1 cited

TTSDS -- Text-to-Speech Distribution Score

Christoph Minixhofer, Ondřej Klejch, Peter Bell

Many recently published Text-to-Speech (TTS) systems produce audio close to real speech. However, TTS evaluation needs to be revisited to make sense of the results obtained with th…

eess.AS2022

Evaluating and reducing the distance between synthetic and real speech distributions

Christoph Minixhofer, Ondřej Klejch, Peter Bell

While modern Text-to-Speech (TTS) systems can produce natural-sounding speech, they remain unable to reproduce the full diversity found in natural speech data. We consider the dist…