ControlSpeech: Towards Simultaneous and Independent Zero-shot Speaker Cloning and Zero-shot Language Style Control

Abstract: In this paper, we present ControlSpeech, a text-to-speech (TTS) system capable of fully cloning the speaker's voice and enabling arbitrary control and adjustment of speaking style. Prior zero-shot TTS models only mimic the speaker's voice without further control and adjustment capabilities while prior controllable TTS models cannot perform speaker-specific voice generation. Therefore, ControlSpeech focuses on a more challenging task: a TTS system with controllable timbre, content, and style at the same time. ControlSpeech takes speech prompts, content prompts, and style prompts as inputs and utilizes bidirectional attention and mask-based parallel decoding to capture codec representations corresponding to timbre, content, and style in a discrete decoupling codec space. Moreover, we analyze the many-to-many issue in textual style control and propose the Style Mixture Semantic Density (SMSD) module, which is based on Gaussian mixture density networks, to resolve this problem. To facilitate empirical validations, we make available a new style controllable dataset called VccmDataset. Our experimental results demonstrate that ControlSpeech exhibits comparable or state-of-the-art (SOTA) performance in terms of controllability, timbre similarity, audio quality, robustness, and generalizability. The relevant code and demo are available at https://github.com/jishengpeng/ControlSpeech .

• The paper introduces ControlSpeech, which achieves simultaneous zero-shot speaker cloning and language style control via a decoupled codec framework.
• It employs bidirectional attention, mask-based parallel decoding, and a Style Mixture Semantic Density model to enable nuanced speech synthesis.
• Experimental results show superior style accuracy, timbre similarity, and audio quality compared to traditional TTS approaches.

ControlSpeech: Simultaneous Zero-shot Speaker Cloning and Style Control in TTS

The paper under discussion introduces ControlSpeech, a text-to-speech (TTS) system that addresses an intricate task in speech synthesis: achieving zero-shot speaker cloning while also providing control over the language style, such as prosody, accent, and emotion. This task is tackled by employing a decoupled codec framework where the synthesis process is based on only a few seconds of an audio prompt combined with a style description, enabling both cloning and control simultaneously. This capability is notably absent from existing TTS models, which tend to either replicate voice characteristics or manipulate speech style, but seldom both.

Methodological Innovations

ControlSpeech builds on contemporary advances in neural codec and generative models. A key innovation is the use of a decoupled codec, which independently represents and processes timbre, content, and style, thereby facilitating independent manipulation of speech characteristics. The model employs a bidirectional attention mechanism and mask-based parallel decoding to handle discrete codec space efficiently, enhancing voice and style controllability.

A notable challenge in the effort to control speech style is addressed through the development of the Style Mixture Semantic Density (SMSD) model. This model tackles the many-to-many mapping issue wherein a specific text style description can correspond to various speech styles. SMSD, based on Gaussian mixture density networks, aids in the nuanced sampling and partitioning of style semantic information, leading to more diverse and expressive speech generation.

Experimental Evaluation

The experimental framework includes the introduction of ControlToolkit, a suite of resources comprising a new dataset (VccmDataset), replicated baseline models, and novel metrics for evaluating control capabilities and synthesized speech quality. The effectiveness of ControlSpeech is compared to prior systems in several respects, including style accuracy, timbre similarity, audio quality, diversity, and generalization across the evaluated datasets.

Quantitative achievements in pitch, speed, energy, and emotion accuracy demonstrate ControlSpeech's superior performance in style controllability compared to existing systems. The results validate the necessity of each component of the model, providing empirical support for the model's architecture. Especially highlighted is the stark improvement over traditional TTS approaches which typically do not simultaneously address voice cloning and style modulation.

Implications and Future Directions

ControlSpeech has significant implications in various domains where personalized TTS systems are crucial, such as content creation, assistive technologies for those with communication impairments, and AI-driven human-computer interaction. The model's potential to extend personalized voice interfaces while maintaining naturalness and expressiveness can significantly enhance user experience.

Future research avenues could pivot towards enriching the diversity of style descriptions to more closely mimic human-level expressions. Furthermore, optimization of the decoupled codec using more sophisticated vector quantization methods might improve the naturalness of synthesized speech. Expanding training datasets to tens of thousands of hours may yield further performance gains. Exploring additional generative architectures could also enhance model robustness and flexibility.

ControlSpeech bridges a critical gap in TTS technology by integrating zero-shot speaker cloning with dynamic style controllability, supported by a robust methodological framework and promising experimental results, setting a new benchmark for future developments in speech synthesis.