arXiv:2607.02002v1 Announce Type: new Abstract: Time-normalized f0 contours of Mandarin words in conversational speech have been shown to be predictable in part from their contextualized embeddings (CEs). The present study investigates whether CEs also predict spoken word duration for 7470 tokens of Mandarin monosyllabic CV words extracted from a Mandarin corpus of spontaneous speech. We show that CEs indeed are predictive for duration, above chance level, not only at the type level, but also at the level of individual tokens, as indicated by the results obtained with the type-wise and token-w
The continuous advancements in AI and specifically in natural language processing and speech synthesis are driving further research into the intricacies of spoken language mechanics.
This research contributes to the fundamental understanding of how AI can model and generate human speech with greater accuracy, impacting the development of more natural and nuanced spoken AI interfaces.
The ability to predict subtle speech features like duration and pitch from contextualized embeddings indicates a deeper level of AI understanding of acoustic properties beyond just word recognition.
- · AI speech synthesis developers
- · Natural language processing researchers
- · Voice AI companies
- · Companies building interactive voice assistants
- · Developers of less nuanced speech synthesis models
- · Traditional phonetics research relying solely on acoustic features
Improved speech synthesis and recognition models with more human-like qualities will emerge.
More seamless and natural human-computer interaction through voice will become commonplace, enhancing user experience.
The enhanced capability for AI to mimic and understand nuanced human speech could lead to new forms of communication and entertainment, potentially blurring lines between human and synthetic voices.
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