MODELS AND METHODS IN MODERN SCIENCE
International scientific-online conference
89
HOW MODERN TECHNOLOGIES (MACHINE TRANSLATION, VOICE
ASSISTANTS) INFLUENCE LANGUAGE STRUCTURE AND
COMMUNICATION
Soliyeva Adiba Qobiljon qizi
Master’s degree student
Faculty: Foreign Language and Literature English
Nordic International University
Email: soliyevaadiba0110@gmail.com
Phone number: +99890-823-69-89
https://doi.org/10.5281/zenodo.15771495
Annotation:
This research explores how machine translation tools and
voice assistants reshape language use and communication patterns. While these
technologies offer convenience and accessibility, they also introduce significant
changes to how individuals structure language, interact, and acquire linguistic
competence. Using theoretical insights and empirical observations, the paper
analyzes both the linguistic and socio-communicative consequences of
interacting with such technologies. The findings suggest a dual impact: improved
access to multilingual communication alongside simplification of language,
altered communication styles, and risks to linguistic expressiveness. The study
emphasizes the need for responsible integration of AI-driven tools in daily
communication to preserve linguistic richness.
Keywords:
Machine Translation, Voice Assistants, Language Simplification,
Communication Patterns, Translationese, Hyperarticulation, Language Learning,
Artificial Intelligence, Linguistic Diversity, Cultural Nuance
Introduction
In today's rapidly developing digital world, our ways of using language and
communicating are changing. One of the biggest drivers of this change is modern
technology, particularly machine translation tools like Google Translate and
voice assistants such as Siri, Alexa, and Google Assistant. These technologies are
becoming an integral part of everyday life, not only for tech-savvy users but also
for a wider range of people - from helping travelers navigate foreign countries to
assisting visually impaired individuals with daily tasks.
However, as useful as they are, they also raise an important question: how
are they changing the way we use language and communicate? The aim of this
research is to examine the influence of machine translation (MT) and voice
assistants (VA) on language structures and forms of communication. The goal is
to understand both the benefits and challenges these technologies bring,
especially in terms of how people speak, write, and interact.
MODELS AND METHODS IN MODERN SCIENCE
International scientific-online conference
90
Literature analyi
s
Machine translation systems are designed to translate text from one
language to another instantly. Over time, these systems have improved
significantly thanks to artificial intelligence, especially neural networks.
However, even the most advanced tools still struggle with complex sentence
structures,
cultural
nuances,
and
idiomatic
expressions.
To get around this, users often simplify their language when using MT tools.
Instead of writing complex, nuanced sentences, they choose shorter, clearer, and
more literal constructions. This practice can gradually affect users’ own
language habits. Research by Bowker and Barlow (2004) notes that repeated
exposure to MT can lead to a form of “translationese”—a style of language that
mimics machine output, often lacking in variety or expressiveness.
Voice assistants like Siri and Alexa have become essential communication
tools for millions of people. They perform tasks, answer questions, and even
engage in simple conversations. However, to work effectively with these
systems, users need to speak in a way that the assistant can understand - often
slowly, clearly, and using standard grammar.
This leads to what linguists call "hyperarticulation" - exaggerating
pronunciation and avoiding informal speech for better machine comprehension.
Additionally, voice assistants often require special command formats. This
reinforces a stereotyped style of language, which over time may limit
spontaneous or creative expression.
One of the biggest problems with both MT and VAs is their weak ability to
handle contextual and cultural features of language. Machines are excellent at
translating literal meaning but are weak at handling humor, irony, sarcasm, and
culturally specific phrases.
Individuals will adapt their language to avoid misunderstanding. While
helpful, it also leads to language flattening. As Anthony Pym (2010) writes, such
simplification may eventually lead to a change in the manner languages evolve.
MODELS AND METHODS IN MODERN SCIENCE
International scientific-online conference
91
On the one hand, MT and VAs have opened up new possibilities for
language learning and intercultural communication. However, there is a
downside. Over-reliance on machines can dissuade learners from learning the
more profound structure of a language. In a study, Garcia and Pena (2011)
warned that language learners who over-rely on MT tools can develop an
unrealistic perception of fluency.
The second major impact is the development of new communication
tendencies in dealing with technology. In communicating with machines,
humans minimize ambiguity, utilize keywords, and avoid open-ended queries.
This type of interaction reflects a more transactional communication style—
keen on getting things done effectively, rather than on building relationships or
expressing emotions.
Methodology
This research adopts a qualitative approach, combining theoretical analysis
with observational data from real-world interactions with MT tools and VAs. The
study focuses on:
- Reviewing academic literature on language simplification, translationese,
and speech adaptation.
- Analyzing user behaviors when interacting with MT tools and VAs, based
on existing research and user testimonials.
- Identifying linguistic patterns influenced by technology, including
structural simplification, reduced idiomatic usage, and shifts in communication
style.
The analysis aims to synthesize these observations to better understand
how technology-mediated communication shapes language structures and
habits.
Results
The findings reveal several key patterns illustrating the influence of MT and
VAs on language and communication:
1. Simplification of Language Structure
s
Frequent users of MT tools tend to modify their language to accommodate
machine limitations. Complex sentence constructions, cultural references, and
idioms are often replaced with simplified, literal phrases. This not only affects
the output but gradually influences users' natural language production,
promoting a more formulaic and less expressive linguistic style.
2. Emergence of "Translationese"
MODELS AND METHODS IN MODERN SCIENCE
International scientific-online conference
92
Bowker and Barlow's (2004) concept of "translationese" is evident in MT-
influenced language. Users exposed to machine-translated texts, especially those
who rely heavily on such tools, often adopt repetitive structures, limited
vocabulary, and a noticeable lack of linguistic variety.
3. Hyperarticulation and Predictable Speech
Voice assistants require clear, standardized speech to function accurately.
As a result, users adapt by articulating slowly, avoiding slang, and using
standardized grammar. While this improves machine comprehension, it fosters
a rigid, command-like communication style, diminishing linguistic spontaneity
(Porcheron et al., 2018).
4
. Cultural and Contextual Limitation
s
MT and VAs struggle with contextual understanding, humor, sarcasm, and
cultural references. To mitigate misunderstandings, users avoid nuanced
language, leading to "language flattening," where expression becomes more
literal and stripped of cultural depth (Pym, 2010).
5. Shifts in Communication Tendencies
Interactions with technology promote transactional communication—
focused on efficiency rather than emotional connection or relationship-building.
Instructing a VA or using MT often revolves around achieving specific outcomes,
contrasting with the richer, more relational nature of human dialogue.
Discussion
The dual impact of MT and VAs reflects both the promises and pitfalls of
technology-mediated communication. On the positive side, these tools
democratize language access, facilitate intercultural communication, and
support language learners. Travelers, for example, can navigate unfamiliar
linguistic landscapes with relative ease, while individuals with disabilities can
interact more independently.
However, these benefits come with significant linguistic and communicative
compromises. Language simplification, translationese, and hyperarticulation
risk eroding linguistic complexity, reducing expressive capacity, and promoting
a transactional over relational communication style. Cultural nuances, vital for
authentic and meaningful interaction, often get lost in machine-mediated
exchanges.
Moreover, excessive reliance on technology may diminish motivation to
learn languages in depth. As Garcia and Pena (2011) emphasize, users may
develop inflated confidence in their linguistic abilities based on machine-
generated outputs, undermining genuine language competence.
MODELS AND METHODS IN MODERN SCIENCE
International scientific-online conference
93
These patterns have broader implications for language evolution. As Pym
(2010) suggests, sustained exposure to simplified, machine-compatible language
can influence how languages change over time, potentially favoring clarity and
standardization over diversity and richness.
Conclusion
Our modern technologies, such as machine translation and voice assistants,
are changing the way we use language and communicate. On the one hand, they
provide powerful tools for translation, clarity, and learning. On the other hand,
they subtly alter our way of speaking and writing, encouraging simpler
constructions, more stereotypical commands, and less cultural refinement.
These situations don't have to be negative, but they need to be addressed.
As users learn to communicate with machines in their own language, the
boundary between human and machine communication begins to disappear.
This can lead to advantages such as more precise and global communication, as
well as risks such as loss of expressiveness, excessive dependence on tools, and
decreased depth of language learning.
To move ahead responsibly, we have to keep these changes in mind and do
everything possible to maintain balance. To this end, we should use technology
as a means of expanding communication and not at the expense of the richness
of human language. Instead, we should use it as a means to enrich our
interactions.
References:
1.Bowker, L., & Barlow, M. (2004). Bilingual text corpora and the translation
process. John Benjamins Publishing.
2.Pym, A. (2010). Exploring translation theories. Routledge.
3.Porcheron, M., Fischer, J. E., Reeves, S., & Sharples, S. (2018). Voice interfaces in
everyday life. Proceedings of the 2018 CHI Conference on Human Factors in
Computing Systems, ACM.
4.Garcia, I., & Pena, M. I. (2011). Machine translation-assisted language learning:
Writing for beginners. Computer Assisted Language Learning , 24(5), 471–487
