EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control (2410.00316v1)

Abstract: While recent advances in Text-to-Speech (TTS) technology produce natural and expressive speech, they lack the option for users to select emotion and control intensity. We propose EmoKnob, a framework that allows fine-grained emotion control in speech synthesis with few-shot demonstrative samples of arbitrary emotion. Our framework leverages the expressive speaker representation space made possible by recent advances in foundation voice cloning models. Based on the few-shot capability of our emotion control framework, we propose two methods to apply emotion control on emotions described by open-ended text, enabling an intuitive interface for controlling a diverse array of nuanced emotions. To facilitate a more systematic emotional speech synthesis field, we introduce a set of evaluation metrics designed to rigorously assess the faithfulness and recognizability of emotion control frameworks. Through objective and subjective evaluations, we show that our emotion control framework effectively embeds emotions into speech and surpasses emotion expressiveness of commercial TTS services.

• The paper introduces EmoKnob, a pioneering framework that manipulates latent speaker embeddings for detailed emotion control in voice cloning.
• It leverages few-shot learning with synthetic and text retrieval methods to generate minimal paired data for embedding diverse emotional nuances.
• Evaluations show enhanced emotion fidelity and user preference, while maintaining high speaker similarity and accurate textual reproduction.

Analysis of "EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Control"

The paper under discussion presents EmoKnob, a novel framework designed for fine-grained emotion control in speech synthesis, specifically within the context of voice cloning technologies. With the increasing capabilities of Text-to-Speech (TTS) systems to produce natural and expressive output, the challenge of incorporating nuanced emotional control becomes paramount. The authors of this paper propose a solution that leverages the few-shot learning paradigm, enabling the synthesis of speech with arbitrary emotional undercurrents using minimal samples.

Framework and Methodology

EmoKnob is structured to operate in conjunction with foundational voice cloning models. It hypothesizes that the latent speaker embedding spaces of these models, when manipulated correctly, can facilitate detailed emotion embedding. The method involves determining an emotion direction vector using paired samples of emotional and neutral speech from the same speaker. Once this vector is identified, it is used to adjust the speaker embedding to reflect the desired emotional state at a user-specified intensity.

Two methodologies are presented for emotion control based on open-ended text descriptions: a synthetic data-based method and a text retrieval-based method. Both approaches exploit recent advances in LLMs, circumventing the dearth of annotated emotional speech datasets. The synthetic method generates emotional audio samples through expressive TTS systems to create synthetic datasets, while the retrieval method employs natural language processing techniques to identify audio resources that inherently match certain emotional descriptors.

Evaluation of EmoKnob

The evaluation of EmoKnob rests on both subjective and objective parameters. Subjective metrics include Emotion Selection Accuracy (ESA), Emotion Enhancement Accuracy (EEA), and Emotion Identification Test (EIT), among others, which are utilized to gauge user perception of emotional fidelity and intensity. Objective measures such as Word Error Rate (WER) and Speaker Similarity (SIM) assess the technical quality of the synthesized speech in relation to its baseline models.

The results indicate that EmoKnob consistently outperforms existing TTS services in delivering emotion-controlled speech, as confirmed by a high percentage of user participants recognizing and preferring the emotions embedded by the framework. The framework maintains speaker identity and text accuracy while effectively overlaying complex emotions like charisma and empathy, which underscores its versatility.

Implications and Future Directions

The introduction of EmoKnob has significant implications for the field of TTS and emotional AI. By providing a robust mechanism for embedding a wide spectrum of emotions into synthesized speech, this framework advances conversational AI systems, making them more adaptive and relatable. This paves the way for applications in entertainment, customer service, and assistive technologies where emotional nuance is vital.

Looking forward, further research can explore scaling EmoKnob for real-time applications and integrating it with more expansive and diverse databases to enhance its robustness. The potential to personalize emotional nuances based on user preferences or contextual requirements remains largely untapped and could be a promising avenue for future exploration.

In summary, by addressing the gap in nuanced emotion control within speech synthesis, EmoKnob introduces an innovative toolset that enhances the expressive capabilities of current voice systems, thereby enriching human-machine interaction.