ReverBERT: A State Space Model for Efficient Text-Driven Speech Style Transfer
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Brown, Michael Martinez, Sofia Singh, Priya |
| author_facet | Brown, Michael Martinez, Sofia Singh, Priya |
| contents | Text-driven speech style transfer aims to mold the intonation, pace, and timbre of a spoken utterance to match stylistic cues from text descriptions. While existing methods leverage large-scale neural architectures or pre-trained language models, the computational costs often remain high. In this paper, we present \emph{ReverBERT}, an efficient framework for text-driven speech style transfer that draws inspiration from a state space model (SSM) paradigm, loosely motivated by the image-based method of Wang and Liu~\cite{wang2024stylemamba}. Unlike image domain techniques, our method operates in the speech space and integrates a discrete Fourier transform of latent speech features to enable smooth and continuous style modulation. We also propose a novel \emph{Transformer-based SSM} layer for bridging textual style descriptors with acoustic attributes, dramatically reducing inference time while preserving high-quality speech characteristics. Extensive experiments on benchmark speech corpora demonstrate that \emph{ReverBERT} significantly outperforms baselines in terms of naturalness, expressiveness, and computational efficiency. We release our model and code publicly to foster further research in text-driven speech style transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_20992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ReverBERT: A State Space Model for Efficient Text-Driven Speech Style Transfer Brown, Michael Martinez, Sofia Singh, Priya Graphics Computation and Language Text-driven speech style transfer aims to mold the intonation, pace, and timbre of a spoken utterance to match stylistic cues from text descriptions. While existing methods leverage large-scale neural architectures or pre-trained language models, the computational costs often remain high. In this paper, we present \emph{ReverBERT}, an efficient framework for text-driven speech style transfer that draws inspiration from a state space model (SSM) paradigm, loosely motivated by the image-based method of Wang and Liu~\cite{wang2024stylemamba}. Unlike image domain techniques, our method operates in the speech space and integrates a discrete Fourier transform of latent speech features to enable smooth and continuous style modulation. We also propose a novel \emph{Transformer-based SSM} layer for bridging textual style descriptors with acoustic attributes, dramatically reducing inference time while preserving high-quality speech characteristics. Extensive experiments on benchmark speech corpora demonstrate that \emph{ReverBERT} significantly outperforms baselines in terms of naturalness, expressiveness, and computational efficiency. We release our model and code publicly to foster further research in text-driven speech style transfer. |
| title | ReverBERT: A State Space Model for Efficient Text-Driven Speech Style Transfer |
| topic | Graphics Computation and Language |
| url | https://arxiv.org/abs/2503.20992 |