🤖 AI Summary
This work addresses the limitations of traditional read-style speech, which lacks the natural prosody required for conversational interaction and struggles to balance naturalness, intelligibility, and real-time performance in applications such as virtual assistants, customer service, and language learning. The authors propose PACC (Prosodic Adjustment with Conversational Context), a novel approach that integrates high-fidelity generative adversarial networks (HiFi-GAN) with context-aware prosody modeling. By leveraging deep neural networks to dynamically adjust intonation, stress, and rhythm, PACC enables high-quality conversion from read speech to natural conversational speech. Experimental results demonstrate that PACC significantly enhances both naturalness and intelligibility across multiple datasets, achieving state-of-the-art mean opinion scores (MOS) and establishing a new benchmark for voice conversion tasks.
📝 Abstract
In recent advancements within speech processing, converting read speech to conversational speech has gained significant attention. The primary challenge in this domain is maintaining naturalness and intelligibility while minimizing computational overhead for real-time applications. Traditional read speech often lacks the nuanced prosodic variation essential for natural conversational interactions, posing challenges for applications in virtual assistants, customer service, and language learning tools. This paper introduces a novel approach, Prosodic Adjustment with Conversational Context (PACC), aimed at converting read speech into natural conversational speech used in various modern applications. PACC utilizes advanced deep neural networks to analyze and modify prosodic features such as intonation, stress, and rhythm. Unlike conventional methods, our approach uses High-Fidelity Generative Adversarial Networks (HiFi-GAN) for speech synthesis. Our experimental results demonstrate significant improvements in speech conversion, enhancing naturalness and achieving better model accuracy with additional training on speech datasets. This research establishes new benchmarks in speech conversion tasks and Mean Opinion Score (MOS) evaluation for testing model accuracy, and we show that our approach can be successfully extended to other speech conversion applications.