teraction. Many recent voice interaction systems have been introduced, allowing users to
communicate with devices on various platforms, such as smartphones (Apple Siri, Google
Cloud, Amazon Alexa, etc.), intelligent cars (BMW, Ford, etc.), and smart homes. In these
systems, one of the essential components is speech synthesis or Text-to-Speech (TTS), which
can convert input text into speech. Developing a TTS system for a language is not only the
implementation of speech processing techniques but also requires linguistic studies such as
phonetics, phonology, syntax, and grammar.
According to statistics in the 25th edition of Ethnologue1 (regarded as the most
comprehensive source of information on linguistic statistics), there are 7,151 living languages
in the world, belonging to 141 language families, of which 2,982 languages are not written.
Some languages have not been described in academic literature, such as dialects of ethnic
minorities. Machine learning methods based on big data do not immediately apply to low-
resourced languages, especially unwritten ones. The low-resourced/unwritten language
processing field has started to pay attention in the past few years and has yet to have many
results. However, the research results of this field are essential because, in addition to bringing
voice communication technologies to ethnic minority communities, products applying this
technology are also essential. It also contributes to the conservation of endangered languages.
Regarding the Vietnamese language and speech processing field, domestic research units
have given it comprehensive attention and addressed various aspects, ranging from natural
language processing problems such as text processing, syntactic component separation, and
semantics to speech processing problems such as synthesis and recognition. However, the
problem of language and speech processing in general, including TTS) systems for minority
languages without a writing system in Vietnam, has not received much attention due to the
scarcity of data sources such as bilingual text data and speech data, as well as a lack of related
linguistic studies.
The Muong language presents unique linguistic characteristics that make it challenging to
develop a TTS system, such as tonality and complex phonetic structures. Therefore, this thesis
aims to fill this gap by focusing on developing a TTS system for the Muong language, a
minority language spoken in Vietnam that does not have a writing system (only the Muong
Hoa Binh dialect had a writing system in 2016). This research area is novel not only in Vietnam
but also worldwide, and the development of a Muong TTS system can contribute to preserving
and promoting this endangered language.