From the 1 of 7 linked papers with an AI index.
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MINT-Bench: A Comprehensive Multilingual Benchmark for Instruction-Following Text-to-Speech
Huakang Chen, Jingbin Hu, Liumeng Xue +12
Instruction-following text-to-speech (TTS) has emerged as an important capability for controllable and expressive speech generation, yet its evaluation remains underdeveloped due t…
VoiceSculptor: Your Voice, Designed By You
Jingbin Hu, Huakang Chen, Linhan Ma +19
Despite rapid progress in text-to-speech (TTS), open-source systems still lack truly instruction-following, fine-grained control over core speech attributes (e.g., pitch, speaking…
ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark
He Wang, Linhan Ma, Dake Guo +4
Automatic Speech Recognition (ASR) has been extensively investigated, yet prior benchmarks have largely focused on assessing the acoustic robustness of ASR models, leaving evaluati…
FlexSpeech: Towards Stable, Controllable and Expressive Text-to-Speech
Linhan Ma, Dake Guo, He Wang +2
Current speech generation research can be categorized into two primary classes: non-autoregressive and autoregressive. The fundamental distinction between these approaches lies in…
WenetSpeech4TTS: A 12,800-hour Mandarin TTS Corpus for Large Speech Generation Model Benchmark
Linhan Ma, Dake Guo, Kun Song +7
With the development of large text-to-speech (TTS) models and scale-up of the training data, state-of-the-art TTS systems have achieved impressive performance. In this paper, we pr…