面向无障碍应用的紧凑型神经TTS语音
Compact Neural TTS Voices for Accessibility
AI总结：
本文提出一种高质量紧凑型神经TTS系统，在低功耗设备上实现约15毫秒低延迟与低磁盘占用，解决了传统无障碍应用语音合成在质量、延迟与存储间的权衡问题。
摘要：
面向无障碍应用的当代文本转语音解决方案通常可分为两类：(i) 基于设备的统计参数语音合成(SPSS)或单元选择(USEL)，以及(ii) 基于云的神经TTS。SPSS和USEL提供低延迟和低磁盘占用，但牺牲了自然度和音频质量。基于云的神经TTS系统提供显著更好的音频质量和自然度，但在延迟和响应性方面出现退化，使其在实际应用中不切实际。最近，神经TTS模型已可部署在手持设备上运行。然而，延迟仍高于SPSS和USEL，同时磁盘占用使得无法同时预装多个语音。在本工作中，我们描述了一种高质量的紧凑型神经TTS系统，实现了约15毫秒的延迟和低磁盘占用。所提出的解决方案能够在低功耗设备上运行。
英文摘要：
Contemporary text-to-speech solutions for accessibility applications can typically be classified into two categories: (i) device-based statistical parametric speech synthesis (SPSS) or unit selection (USEL) and (ii) cloud-based neural TTS. SPSS and USEL offer low latency and low disk footprint at the expense of naturalness and audio quality. Cloud-based neural TTS systems provide significantly better audio quality and naturalness but regress in terms of latency and responsiveness, rendering these impractical for real-world applications. More recently, neural TTS models were made deployable to run on handheld devices. Nevertheless, latency remains higher than SPSS and USEL, while disk footprint prohibits pre-installation for multiple voices at once. In this work, we describe a high-quality compact neural TTS system achieving latency on the order of 15 ms with low disk footprint. The proposed solution is capable of running on low-power devices.
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