Generating Emotionally Expressive and Disfluent Speech for Conversational AI Systems

Core Concepts

A novel speech synthesis pipeline that generates emotional and disfluent speech patterns in a zero-shot manner using a large language model, enabling more natural and relatable interactions for conversational AI systems.

Abstract

The content describes a novel approach to humanize machine communication by generating emotional and disfluent speech patterns in a zero-shot manner using a large language model.
The key highlights are:

Contemporary conversational systems often lack the emotional depth and disfluent characteristics of human interactions, making them seem mechanical and less relatable.
The proposed method uses a large language model (GPT-4) to generate responses with varying levels of emotion and disfluency cues through careful prompt tuning, in a zero-shot fashion.
The generated text is then converted to speech using a rule-based approach that maps the emotional cues and disfluencies to corresponding speech patterns and sounds.
The method is evaluated in the context of a virtual patient scenario for SBIRT (Screening, Brief Intervention, and Referral to Treatment) training, where realistic emotional expression is crucial for effective healthcare training.
Experiments show that the synthesized speech is almost indistinguishable from genuine human communication, making the interactions more personal and authentic.

Stats

"I am very sad, I would like to have a cup of coffee"
"sighs heavily I am very sad, whispers I would l-like to have a cup of coffee"

Quotes

sighs heavily
whispers
cries softly
looks down
sobs
nods slowly
bursts into tears

Key Insights Distilled From

Humane Speech Synthesis through Zero-Shot Emotion and Disfluency Generation

by Rohan Chaudh... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01339.pdf

Deeper Inquiries

How can the proposed approach be extended to generate more diverse and context-aware emotional and disfluent patterns?

To enhance the diversity and context-awareness of emotional and disfluent patterns in speech synthesis, the proposed approach can be extended in several ways:

Incorporating a Larger Emotional Vocabulary: Expanding the emotional cues mapping to include a wider range of emotions and intensities can make the speech synthesis more nuanced and realistic.
Dynamic Disfluency Generation: Implementing a dynamic disfluency generation system that adapts to the context of the conversation can make the speech more natural. This could involve analyzing the flow of the conversation to determine where disfluencies would naturally occur.
Personalization: Tailoring the emotional and disfluent patterns based on the user's preferences or characteristics can make the interactions more personalized and engaging.
Real-time Feedback: Incorporating real-time feedback mechanisms to adjust the emotional and disfluent patterns based on the user's responses can make the conversations more interactive and responsive.

What are the potential ethical concerns and limitations of using such technology to generate human-like speech, and how can they be addressed?

Ethical concerns and limitations of using technology to generate human-like speech include:

Misrepresentation: Clear disclaimers should be provided to ensure transparency and prevent users from being misled into believing they are interacting with a human.
Emotion Manipulation: Regulations should be in place to prevent the intentional manipulation of emotions through speech synthesis for unethical purposes.
Bias and Stereotyping: Careful consideration should be given to the data used to train the models to avoid perpetuating biases or stereotypes in the generated speech.
To address these concerns, robust guidelines, regulations, and oversight mechanisms should be established to ensure responsible and ethical use of human-like speech synthesis technology.

How can the integration of emotional and disfluent speech synthesis be leveraged to enhance the overall user experience and engagement in conversational AI systems beyond healthcare training?

The integration of emotional and disfluent speech synthesis can enhance user experience and engagement in conversational AI systems in various ways:

Personalization: Tailoring the emotional and disfluent patterns to match the user's preferences can create a more personalized and engaging interaction.
Empathy and Understanding: By accurately conveying emotions and disfluencies, the AI system can demonstrate empathy and better understand the user's feelings and needs.
Improved Communication: Natural speech patterns with emotions and disfluencies can lead to more effective communication and a deeper connection between the user and the AI system.
Enhanced User Satisfaction: Users are more likely to be satisfied with the interaction if the AI system can convey emotions and disfluencies authentically, leading to increased engagement and loyalty.