We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for
Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work
addresses the gap in linguistic technology for underrepresented languages by
collecting and processing data to build a speech synthesis system. We begin our
study by gathering a substantial dataset of Mundari text and speech and train
end-to-end speech models. We also delve into the methods used for training our
models, ensuring they are efficient and effective despite the data constraints.
We evaluate our system with native speakers and objective metrics,
demonstrating its potential as a tool for preserving and promoting the Mundari
language in the digital age.

针对孟达里语，这篇研究论文介绍了 MunTTS，一个专门用于文本到语音（TTS）的端到端系统，通过收集和处理数据来构建一个语音合成系统，以填补低资源印度语言在语言技术方面的不足，并通过与母语者和客观指标的评估展示其在数字时代中作为保护和推广孟达里语的工具的潜力。

MunTTS: 一个 Mundari 语文到语音系统

MunTTS: A Text-to-Speech System for Mundari

Phrase break prediction is a crucial task for improving the prosody
naturalness of a text-to-speech (TTS) system. However, most proposed phrase
break prediction models are monolingual, trained exclusively on a large amount
of labeled data. In this paper, we address this issue for low-resource
languages with limited labeled data using cross-lingual transfer. We
investigate the effectiveness of zero-shot and few-shot cross-lingual transfer
for phrase break prediction using a pre-trained multilingual language model. We
use manually collected datasets in four Indo-European languages: one
high-resource language and three with limited resources. Our findings
demonstrate that cross-lingual transfer learning can be a particularly
effective approach, especially in the few-shot setting, for improving
performance in low-resource languages. This suggests that cross-lingual
transfer can be inexpensive and effective for developing TTS front-end in
resource-poor languages.

本文采用跨语言迁移学习的方式，使用预训练的多语言语言模型，在少量标注数据的情况下，研究了短语断点预测的效果，发现跨语言迁移可以是一种有效的方法，尤其是在低资源语言中，因此建议使用跨语言迁移来改善语音合成前端。

多语言语言模型的跨语言短语断点预测迁移学习

Cross-Lingual Transfer Learning for Phrase Break Prediction with  Multilingual Language Model

Recognizing written domain numeric utterances (e.g. I need $1.25.) can be
challenging for ASR systems, particularly when numeric sequences are not seen
during training. This out-of-vocabulary (OOV) issue is addressed in
conventional ASR systems by training part of the model on spoken domain
utterances (e.g. I need one dollar and twenty five cents.), for which numeric
sequences are composed of in-vocabulary numbers, and then using an FST
verbalizer to denormalize the result. Unfortunately, conventional ASR models
are not suitable for the low memory setting of on-device speech recognition.
E2E models such as RNN-T are attractive for on-device ASR, as they fold the AM,
PM and LM of a conventional model into one neural network. However, in the
on-device setting the large memory footprint of an FST denormer makes spoken
domain training more difficult. In this paper, we investigate techniques to
improve E2E model performance on numeric data. We find that using a
text-to-speech system to generate additional numeric training data, as well as
using a small-footprint neural network to perform spoken-to-written domain
denorming, yields improvement in several numeric classes. In the case of the
longest numeric sequences, we see reduction of WER by up to a factor of 8.

本文研究使用文本到语音系统和小型神经网络来解决在设备上语音识别中模型训练的问题，以提高 E2E 模型在数字数据上的性能表现。