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Meet PHEME: PolyAI’s Advanced Transformer-Based TTS System for Efficient and Conversational Synthesis

Recent advancements in speech generation have led to remarkable progress, with the introduction of the PHEME TTS system by PolyAI. The system focuses on achieving lifelike speech synthesis for modern AI applications, emphasizing adaptability, efficiency, and high-quality conversational capabilities. Comparative results demonstrate PHEME’s superior performance in terms of efficiency and synthesis quality.

 Meet PHEME: PolyAI’s Advanced Transformer-Based TTS System for Efficient and Conversational Synthesis

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PHEME: PolyAI’s Advanced Transformer-Based TTS System for Efficient and Conversational Synthesis

In recent years, there has been significant progress in speech generation, particularly in achieving lifelike speech synthesis for conversational systems like voice assistants. PolyAI has developed PHEME, a Transformer-based TTS system, to address the challenges of conversational synthesis and efficiency.

Key Features of PHEME:

  • High-quality TTS in multi-speaker and single-speaker scenarios
  • Emphasis on natural prosody and adaptability
  • Focus on compact, shareable models with high inference efficiency

Methodology:

PHEME employs speech tokenization, T2S training and inference, and A2S training and inference to achieve its goals. The system utilizes a T5-style encoder-decoder architecture and a non-autoregressive procedure for speech generation.

Performance and Efficiency:

Comparative results with MQTTS show that PHEME outperforms in terms of Word Error Rate and Mel-cepstral Distortion while offering faster non-autoregressive inference. The system also demonstrates significant efficiency benefits in terms of inference speed.

Value Proposition:

PHEME aims to create high-quality, production-friendly, and efficient conversational systems, offering scalability and improved synthesis quality. It serves as a foundation for further developments in conversational TTS systems, focusing on synthesis quality and efficiency.

For more information, check out the Paper and Project.

Practical AI Solutions for Middle Managers

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  2. Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
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  4. Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.

For AI KPI management advice, connect with us at hello@itinai.com. And for continuous insights into leveraging AI, stay tuned on our Telegram or Twitter.

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Vladimir Dyachkov, Ph.D
Editor-in-Chief itinai.com

I believe that AI is only as powerful as the human insight guiding it.

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