Exploring an Inter-Pausal Unit (IPU) based Approach for Indic End-to-End TTS Systems

Anusha Prakash, Hema A Murthy
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引用次数: 0

Abstract

Sentences in Indian languages are generally longer than those in English. Indian languages are also considered to be phrase-based, wherein semantically complete phrases are concatenated to make up sentences. Long utterances lead to poor training of text-to-speech models and result in poor prosody during synthesis. In this work, we explore an inter-pausal unit (IPU) based approach in the end-to-end (E2E) framework, focusing on synthesising conversational-style text. We consider both autoregressive Tacotron2 and non-autoregressive FastSpeech2 architectures in our study and perform experiments with three Indian languages, namely, Hindi, Tamil and Telugu. With the IPU-based Tacotron2 approach, we see a reduction in insertion and deletion errors in the synthesised audio, providing an alternative approach to the FastSpeech(2) network in terms of error reduction. The IPU-based approach requires less computational resources and produces prosodically richer synthesis compared to conventional sentence-based systems.
探索基于因果单元(IPU)的端到端智能语音识别系统方法
印度语言的句子通常比英语的句子长。印度语言也被认为是以短语为基础的语言,语义完整的短语被连接起来构成句子。长语句导致文本到语音模型的训练时间过长,并造成合成时的前音不佳。在这项工作中,我们在端到端(E2E)框架内探索了一种基于停顿间单元(IPU)的方法,重点是合成对话式文本。我们在研究中考虑了自回归 Tacotron2 和非自回归 FastSpeech2 架构,并对三种印度语言(印地语、泰米尔语和泰卢固语)进行了实验。通过基于 IPU 的 Tacotron2 方法,我们发现合成音频中的插入和删除错误有所减少,在减少错误方面为 FastSpeech(2) 网络提供了一种替代方法。与传统的基于句子的系统相比,基于 IPU 的方法所需的计算资源更少,合成的前音也更丰富。
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