Speech technologies rely on capturing a speaker's voice variability while
obtaining comprehensive language information. Textual prompts and sentence
selection methods have been proposed in the literature to comprise such
adequate phonetic data, referred to as a phonetically rich \textit{corpus}.
However, they are still insufficient for acoustic modeling, especially critical
for languages with limited resources. Hence, this paper proposes a novel
approach and outlines the methodological aspects required to create a
\textit{corpus} with broad phonetic coverage for a low-resourced language,
Brazilian Portuguese. Our methodology includes text dataset collection up to a
sentence selection algorithm based on triphone distribution. Furthermore, we
propose a new phonemic classification according to acoustic-articulatory speech
features since the absolute number of distinct triphones, or low-probability
triphones, does not guarantee an adequate representation of every possible
combination. Using our algorithm, we achieve a 55.8\% higher percentage of
distinct triphones -- for samples of similar size -- while the currently
available phonetic-rich corpus, CETUC and TTS-Portuguese, 12.6\% and 12.3\% in
comparison to a non-phonetically rich dataset.

本研究介绍了一种用于创建巴西葡萄牙语语音覆盖度广泛的语音语料库的新方法，并提出了基于声音 - 口型语音特征的新的音素分类方法，通过使用该算法，我们使得与目前可用的语音丰富语料库相比，一种样本中不同音素的百分比增加了 55.8％。

低资源语言的音标丰富语料构建

Phonetically rich corpus construction for a low-resourced language

Te reo Māori (referred to as Māori), New Zealand's indigenous language,
is under-resourced in language technology. Māori speakers are bilingual,
where Māori is code-switched with English. Unfortunately, there are minimal
resources available for Māori language technology, language detection and
code-switch detection between Māori-English pair. Both English and Māori
use Roman-derived orthography making rule-based systems for detecting language
and code-switching restrictive. Most Māori language detection is done
manually by language experts. This research builds a Māori-English bilingual
database of 66,016,807 words with word-level language annotation. The New
Zealand Parliament Hansard debates reports were used to build the database. The
language labels are assigned using language-specific rules and expert manual
annotations. Words with the same spelling, but different meanings, exist for
Māori and English. These words could not be categorised as Māori or English
based on word-level language rules. Hence, manual annotations were necessary.
An analysis reporting the various aspects of the database such as metadata,
year-wise analysis, frequently occurring words, sentence length and N-grams is
also reported. The database developed here is a valuable tool for future
language and speech technology development for Aotearoa New Zealand. The
methodology followed to label the database can also be followed by other
low-resourced language pairs.

本研究旨在构建一个 Māori-English 双语数据库并进行语言标注，该数据库针对新西兰母语使用者的双语特点和语言切换进行了设计，并分析了该数据库的各种数据。该研究对于新西兰的语言和语音技术发展是一个有价值的工具，而且其方法可以为其他类似的少资源语言提供参考。

用于语言技术的标记 Māori-English 双语数据库的开发

The Development of a Labelled te reo Māori-English Bilingual Database for Language Technology

Despite the significant progress in end-to-end (E2E) automatic speech
recognition (ASR), E2E ASR for low resourced code-switching (CS) speech has not
been well studied. In this work, we describe an E2E ASR pipeline for the
recognition of CS speech in which a low-resourced language is mixed with a high
resourced language. Low-resourcedness in acoustic data hinders the performance
of E2E ASR systems more severely than the conventional ASR systems.~To mitigate
this problem in the transcription of archives with code-switching Frisian-Dutch
speech, we integrate a designated decoding scheme and perform rescoring with
neural network-based language models to enable better utilization of the
available textual resources. We first incorporate a multi-graph decoding
approach which creates parallel search spaces for each monolingual and mixed
recognition tasks to maximize the utilization of the textual resources from
each language. Further, language model rescoring is performed using a recurrent
neural network pre-trained with cross-lingual embedding and further adapted
with the limited amount of in-domain CS text. The ASR experiments demonstrate
the effectiveness of the described techniques in improving the recognition
performance of an E2E CS ASR system in a low-resourced scenario.

本文研究了一种面向代码交错语音的端到端语音识别技术，并利用神经网络语言模型和循环神经网络提高低资源情况下的识别性能。