Speak, Edit, Repeat: High-Fidelity Voice Editing and Zero-Shot TTS with Cross-Attentive Mamba
Abstract
We introduce MAVE (Mamba with Cross-Attention for Voice Editing and Synthesis), a novel autoregressive architecture for text-conditioned voice editing and high-fidelity text-to-speech (TTS) synthesis, built on a cross-attentive Mamba backbone. MAVE achieves state-of-the-art performance in speech editing and very competitive results in zero-shot TTS, while not being explicitly trained on the latter task, outperforming leading autoregressive and diffusion models on diverse, real-world audio. By integrating Mamba for efficient audio sequence modeling with cross-attention for precise text-acoustic alignment, MAVE enables context-aware voice editing with exceptional naturalness and speaker consistency. In pairwise human evaluations on a random 40-sample subset of the RealEdit benchmark (400 judgments), 57.2% of listeners rated MAVE - edited speech as perceptually equal to the original, while 24.8% prefered the original and 18.0% MAVE - demonstrating that in the majority of cases edits are indistinguishable from the source. MAVE compares favorably with VoiceCraft and FluentSpeech both on pairwise comparisons and standalone mean opinion score (MOS) evaluations. For zero-shot TTS, MAVE exceeds VoiceCraft in both speaker similarity and naturalness, without requiring multiple inference runs or post-processing. Remarkably, these quality gains come with a significantly lower memory cost and approximately the same latency: MAVE requires ~6x less memory than VoiceCraft during inference on utterances from the RealEdit database (mean duration: 6.21s, A100, FP16, batch size 1). Our results demonstrate that MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the synergistic integration of structured state-space modeling and cross-modal attention.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the integration of structured state-space modeling and cross-modal attention. The paper presents a compelling advancement in the field of audio processing, demonstrating significant improvements in both efficiency and quality, although it would benefit from enhanced reproducibility measures and a discussion of ethical implications.
Methodology
The paper presents MAVE, an autoregressive architecture that integrates a cross-attentive mechanism with a Mamba backbone for voice editing and TTS synthesis. The methodology is well-structured, leveraging state-space modeling and cross-modal attention to achieve high fidelity in voice editing and synthesis. The use of cross-attention for text-acoustic alignment is particularly innovative, allowing for context-aware modifications to audio. The autoregressive nature of the model also suggests a thoughtful approach to sequence generation, although details on the training regimen and hyperparameter tuning could enhance the understanding of the model's performance.
Results & evaluation
The experiments are robust, utilizing a variety of benchmarks, including the RealEdit dataset, to evaluate the model's performance. The human evaluation metrics, including pairwise comparisons and MOS scores, provide a comprehensive view of the model's effectiveness. The results indicate that MAVE not only matches but often exceeds existing models like VoiceCraft and FluentSpeech, particularly in terms of memory efficiency and naturalness. However, the paper could benefit from a more detailed analysis of the datasets used and the statistical significance of the results.
Reproducibility
The paper lacks sufficient detail regarding the implementation specifics, such as the training process, data preprocessing, and evaluation metrics, which are crucial for reproducibility. While the architecture is described, the absence of code or a demo URL limits the ability for other researchers to replicate the findings. Including a link to a GitHub repository or supplementary materials would significantly enhance reproducibility.
Limitations
One limitation is the reliance on subjective human evaluations, which can introduce variability and bias. Additionally, while the model shows promise in zero-shot TTS, the performance on diverse speaker characteristics and accents remains unexplored. The paper does not address potential ethical concerns related to voice synthesis technology, such as misuse in deepfakes or privacy violations.
Broader impact
The implications of MAVE are significant, particularly in applications like voice dubbing, personalized voice assistants, and content creation. The ability to edit voice recordings seamlessly has the potential to revolutionize industries reliant on audio content. However, the technology also raises ethical questions regarding consent and the potential for misuse, necessitating careful consideration in its deployment. MAVE establishes a new standard for flexible, high-fidelity voice editing and synthesis through the integration of structured state-space modeling and cross-modal attention. The paper presents a compelling advancement in the field of audio processing, demonstrating significant improvements in both efficiency and quality, although it would benefit from enhanced reproducibility measures and a discussion of ethical implications.
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