Xuezhe Ma
Papers
1
Total Citations
38
H-Index
1
About
Xuezhe Ma is a leading researcher in natural language processing and speech technology, with a focus on improving the expressiveness and naturalness of text-to-speech (TTS) systems. His early groundbreaking work on "Automatic prosody prediction and detection with Conditional Random Field (CRF) models" (2010, 38 citations) addressed a critical challenge in TTS: the robotic, unnatural prosody that persists even when segmental quality is high. By applying CRF models to predict and detect prosodic boundaries and stress patterns, Ma demonstrated how structured prediction techniques can significantly enhance the rhythmic and intonational flow of synthesized speech. This contribution laid important groundwork for more expressive TTS systems, bridging the gap between acoustic accuracy and human-like delivery. His research has influenced subsequent work in prosody modeling, sequence labeling, and speech synthesis, earning recognition for tackling one of the most persistent hurdles in voice user interfaces. Ma’s work remains a key reference for researchers aiming to make machine speech sound truly natural.
Research Focus
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Top Papers
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