Speech Transcription Research Articles

THESAURUS-I OF THE SECONDARY LANGUAGE PERSONALITY REPRESENTED IN THE ECONOMIC TEXTS IN ENGLISH

This article is devoted to the study of the verbal-semantic level (Thesaurus-I) of the secondary linguistic personality based on the analysis of texts on economic and financial topics. 8 texts (17,348 words, 97,712 characters, 132 minutes 36 seconds), which are transcripts of a monologue public speech by non-native English speakers at a TED conference, and 10 texts (19,227 words, 110,133 characters, 125 minutes 44 seconds), which are transcripts of native English speakers’ speeches, are served as the material for this study. The paper presents the results of a statistical analysis of the use of words frequency, proving the fact that Thesaurus-I of the secondary linguistic personality under analysis corresponds to the verbal-semantic level of native speakers. Further, the results of a statistical analysis of the use of Word Classes frequency are given indicating that the analyzed texts are a reflection of the real English language picture of the world. In addition, a thesaurus of English texts of an economic orientation has been built and analyzed, which allows describing the main characteristics of an English language picture of the world fragment, verbalized in texts of an economic orientation, and highlighting its inherent archetypes.

From Thunberg to the L2 Classroom: Public Speaking Techniques

To participate in the global conversation on how to combat climate change, Japanese university students need to better utilise their English for Academic Purposes (EAP) skills outside the second language (L2) classroom. Therefore, this study seeks to enhance the public speaking skills of Japanese university students by analysing and drawing from a series of recent speeches by Swedish climate-change activist Greta Thunberg. Being of a similar age and speaking in a second language, Thunberg resonates as a public speaker that Japanese university students can model. The research design for this study consisted of two phases. Initially, a structural analysis of Thunberg’s speech transcripts was conducted, exploring her use of positive and negative message framing approaches. Secondly, an analysis of the persuasive discursive techniques (PDTs) used in her speeches was conducted, focusing on identifying established rhetorical devices such as tripling, contrasting, and personalising, as well as the use of inclusive language and exclusive language. This paper details and analyses the approaches and techniques used by Thunberg (framing and rhetorical) and then discusses how they can be taught to Japanese university students seeking to move beyond generic English language classroom presentations.

LINGUISTIC PECULARITIES OF REALISATION OF PERSUASIVE STRATEGIES IN MOTIVATIONAL TED TALKS SPEECHES

The paper presents the analysis of linguistic means of realization of persuasive strategies in motivational Ted Talks speeches. Nowadays, the popularity of Ted Talks gradually increases due to the variety of meticulously organized speeches presented to the view of society since the moment of TED’s creation. Millions of people from all around the world visit the online TED platform daily in search of inspiration, motivation and knowledge from various spheres. The object of our scientific research is the persuasive strategies in motivational Ted Talks speeches. The relevance of the study is determined by the need to identify and study the mechanisms of verbal realization of persuasive strategies in rhetorical and media discourse, as persuasion in discourse is one of the current problems of modern linguistics. The subject of the research is verbal means of realization of persuasive strategies in the discursive space of Ted Talks speeches. In the research the following methods have been used: the methods of analysis and deduction; the method of pragmatic analysis, the method of contextual analysis, and the descriptive method. The data of the research has been withdrawn from the transcripts of motivational Ted Talks speeches. The scientific novelty of the results obtained is in singling out the peculiarities of persuasive strategies realization in Ted Talks media discourse and working out the typology of persuasive strategies. The study is based on the three persuasive strategies outlined by the principle of rhetoric: ethos, logos, and pathos. Together, they support efficient persuasion and present the tools to effectively connect with the audience; know their interests, prejudices and expectations. Ethos is a persuasive strategy that allows an orator to establish their authority to speak on the subject, logos is categorized as a tool for implementing a logical argument into the speech to prove its point and pathos is a speaker’s attempt to influence the audience emotionally. The results prove that all three of those rhetorical persuasive strategies are frequently used by Ted Talks presenters in their motivational speeches. The appeals to credibility, logic and emotions are the key features implemented in the discursive space of Ted Talks as the necessary elements of persuasion. Key words: persuasive strategies, ethos, pathos, logos, motivational speeches, Ted Talks.

Power and Positivity: Psycholinguistic Perspectives on Word Valence in Canadian Parliament

Politicians are skilled language users who deploy words strategically and pay close attention to the emotions that those words evoke. We examined the emotional characteristics of over 92 million words spoken by Canadian Members of Parliament between 2006 and 2021. The analysis brought together the Warriner, Kuperman, and Brysbaert (Behav. Res., 2013, 45, 1191–1207) database of valence (positivity) ratings for English and the Canadian Hansard, which contains a transcription of parliamentary speech. Results revealed that the positivity of words used by politicians in parliament was significantly related to both political and social variables. Politicians increased the positivity of their language after the onset of the COVID-19 crisis. Within the time of the crisis, word positivity was linked statistically to month-by-month case counts, indicating a very fine-grained sensitivity to social realities. Our analysis also revealed a fine-grained sensitivity of word valence to political realities. As expected, parties in power used more positive language than those in opposition. In addition, our analysis revealed that individual parties have characteristic levels of word positivity and that those levels change in accordance with political changes as specific as whether or not the party in power holds a majority of seats in parliament. These findings suggest that the emotional properties of words used by Members of Parliament are reliably indexed to sociopolitical dynamics. The findings also suggest that the methodology of linking individual word ratings to Hansard Documents (which are used to document Parliamentary activities in over 25 countries) can provide a key tool for the understanding of specific crises such as the COVID-19 global pandemic as well as more general social and political trends across countries and languages.

A Linguistic Profile of Narrative Speech in Early and Late-Onset Alzheimer’s Disease

Objective. Alzheimer’s Dementia is defined as multiple impairments in cognitive functions and language impairment is one of the observed problems in this disease. In order to analyse these problems, narrative speech transcriptions of Turkish patients with early and late-onset Alzheimer’s Disease (AD) and an age/education-matched control group were examined.
 Materials & Methods. By using different language tests, the sentence production of Turkish patients with early (n: 23) and late-onset (n: 39) was examined and the results were compared within groups and within language tests. Our aim was to reveal the differences in sentence processing of Turkish Alzheimer patients. 
 Results. According to the results, Turkish Alzheimer patients use short and simple sentences composed of fewer words in their speech. It was determined that these patients performed better in Picnic Picture Description test, which is thought to be easy to describe. In Cookie Theft Picture Description test, which is a complex one, and in the Subject-based Narration test, it was found that the speech amounts of AD patients were less. It was revealed that early-onset AD patients used shorter sentences including fewer words compared to late-onset AD patients. 
 Conclusion. In the light of the data obtained, it can be said that AD patients have different sentence processing than normal individuals because of the problems in the language and memory areas of the disease. The fact that sentence processing is different means that these patients do not prefer the types of sentences that normal individuals prefer in their speeches, and use less words and simpler sentences.

IDEOLOGY OF JOKOWI AS INDONESIAN PRESIDENT IN CRITICAL DISCOURSE ANALYSIS STUDY

People as social human beings need a language for communication in daily activities. Communication can be found in many forms, one of them is speech. Thus, related to speech and the function of the speech to society the writer is interested in analysing the language, power and ideology in the speech of President Jokowi’s. This study uses critical discourse analysis theory, where it can be found that ideology can be revealed with the use of vocabulary, grammar and text structure of the speaker. The writer uses qualitative method by analysing Jokowi’s speech transcript. The writer also uses identity method by applying sociolinguistics method and pragmatic method. For presenting the data, the writer uses informal method. The writer found eight ideologies in analysing Jokowi’s speech such as the ideology of equality, the ideology in unity, the ideology to honour the law, the ideology of social justice, the ideology in prioritizing citizen, the ideology of national prosperity, the ideology of hard working, and the ideology in assertiveness. The result of this study shows a proof about the relationship between language, power, and ideology. As an Indonesian president, Jokowi has the power to choose the utterance in his speech. Through power over language, Jokowi can reveal the ideology that he holds so that the citizens can understand and follow his ideology.

A Hybrid Speech Enhancement Algorithm for Voice Assistance Application.

In recent years, speech recognition technology has become a more common notion. Speech quality and intelligibility are critical for the convenience and accuracy of information transmission in speech recognition. The speech processing systems used to converse or store speech are usually designed for an environment without any background noise. However, in a real-world atmosphere, background intervention in the form of background noise and channel noise drastically reduces the performance of speech recognition systems, resulting in imprecise information transfer and exhausting the listener. When communication systems’ input or output signals are affected by noise, speech enhancement techniques try to improve their performance. To ensure the correctness of the text produced from speech, it is necessary to reduce the external noises involved in the speech audio. Reducing the external noise in audio is difficult as the speech can be of single, continuous or spontaneous words. In automatic speech recognition, there are various typical speech enhancement algorithms available that have gained considerable attention. However, these enhancement algorithms work well in simple and continuous audio signals only. Thus, in this study, a hybridized speech recognition algorithm to enhance the speech recognition accuracy is proposed. Non-linear spectral subtraction, a well-known speech enhancement algorithm, is optimized with the Hidden Markov Model and tested with 6660 medical speech transcription audio files and 1440 Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) audio files. The performance of the proposed model is compared with those of various typical speech enhancement algorithms, such as iterative signal enhancement algorithm, subspace-based speech enhancement, and non-linear spectral subtraction. The proposed cascaded hybrid algorithm was found to achieve a minimum word error rate of 9.5% and 7.6% for medical speech and RAVDESS speech, respectively. The cascading of the speech enhancement and speech-to-text conversion architectures results in higher accuracy for enhanced speech recognition. The evaluation results confirm the incorporation of the proposed method with real-time automatic speech recognition medical applications where the complexity of terms involved is high.

Automatic Speech Classifier for Mild Cognitive Impairment and Early Dementia

The World Health Organization estimates that 50 million people are currently living with dementia worldwide and this figure will almost triple by 2050. Current pharmacological treatments are only symptomatic, and drugs or other therapies are ineffective in slowing down or curing the neurodegenerative process at the basis of dementia. Therefore, early detection of cognitive decline is of the utmost importance to respond significantly and deliver preventive interventions. Recently, the researchers showed that speech alterations might be one of the earliest signs of cognitive defect, observable well in advance before other cognitive deficits become manifest. In this article, we propose a full automated method able to classify the audio file of the subjects according to the progress level of the pathology. In particular, we trained a specific type of artificial neural network, called autoencoder, using the visual representation of the audio signal of the subjects, that is, the spectrogram. Moreover, we used a data augmentation approach to overcome the problem of the large amount of annotated data usually required during the training phase, which represents one of the most major obstacles in deep learning. We evaluated the proposed method using a dataset of 288 audio files from 96 subjects: 48 healthy controls and 48 cognitively impaired participants. The proposed method obtained good classification results compared to the state-of-the-art neuropsychological screening tests and, with an accuracy of 90.57%, outperformed the methods based on manual transcription and annotation of speech.

A linguistic profile of narrative speech in early and late Alzheimer's disease

Objective. Alzheimer’s Dementia is defined as multiple impairments in cognitive functions and language impairment is one of the observed problems in this disease. In order to analyse these problems, narrative speech transcriptions of Turkish patients with early and late-onset Alzheimer’s Disease (AD) and an age/education-matched control group were examined. Materials & Methods. By using different language tests, the sentence production of Turkish patients with early (n: 23) and late-onset (n: 39) was examined and the results were compared within groups and within language tests. Our aim was to reveal the differences in sentence processing of Turkish Alzheimer patients. Results. According to the results, Turkish Alzheimer patients use short and simple sentences composed of fewer words in their speech. It was determined that these patients performed better in Picnic Picture Description test, which is thought to be easy to describe. In Cookie Theft Picture Description test, which is a complex one, and in the Subject-based Narration test, it was found that the speech amounts of AD patients were less. It was revealed that early-onset AD patients used shorter sentences including fewer words compared to late-onset AD patients. Conclusion. In the light of the data obtained, it can be said that AD patients have different sentence processing than normal individuals because of the problems in the language and memory areas of the disease. The fact that sentence processing is different means that these patients do not prefer the types of sentences that normal individuals prefer in their speeches, and use less words and simpler sentences.

Understanding Malay Corpora: A Content Analysis of 15 Malay Corpora

Corpus research becomes an important area of research of late, especially in Malaysia and for the national language, Malay language. A corpus includes texts and transcriptions of speeches for variety of situations. For this short paper, the focus is on Malay language, which is the national and official language of Malaysia. The purposes of this paper are to identify features and types of Malay Corpora and to determine the needs for a military biased Malay Corpus. In so doing, as a short paper, the methodology involves only content analysis of relevant documents on the development of Malay language corpora. Preliminary findings suggest that there are at least 15 Malay corpora in existence, and that some of the features in these corpora overlap. Further, the researchers argue for the need for a Malay Corpus for Military Operations since the existing corpora do not fully cater for this type of corpus.

High quality recordings and transcriptions of speech via remote platforms

High quality recordings and transcriptions of speech are important to a wide variety of disciplines from linguistics and human language technology to biomedical screening, diagnosis and tracking. The wide availability of internet connections and powerful mobile devices offers low cost opportunities for collecting speech data at scale. But even in the era of open source software, numerous challenges remain. For example, video call applications such as Zoom have become widespread and allow for recording, but only provide lossy codecs with low frame rates, and often contain missing, repeated, or interpolated frames, as well as freezes, longer dropouts and other audio artefacts. LDC has recently developed a suite of tools to allow high quality internet-based audio recordings and transcription, with a premium on portability and flexibility, using secure cloud computing services for storage and back-end processing. We present here the current design and capabilities of our software, as well as availability in terms of open source code and app distribution. We also discuss future plans; planned capabilities include 2 + sided conversational recordings, connecting participants via the internet, a modern extension of Conversational Telephone Speech (CTS) collections.

An automatic Alzheimer’s disease classifier based on spontaneous spoken English

According to the World Health Organization, the number of people suffering from dementia worldwide will grow to 150 million by mid-century, and Alzheimer’s disease is the most common form of dementia contributing to 60%–70% of cases. The problem is compounded by the fact that current pharmacologic treatments are only symptomatic, and therapies are ineffective in slow down or cure the degenerative process. An automatic and standardize classifier for Alzheimer’s disease is thereby extremely important to rapidly respond and deliver as preventive as possible interventions. Speech alterations might be one of the earliest signs of cognitive defect and, recently, the researchers showed that they can be observable well in advance other cognitive deficits become manifest. In this paper, we propose a full automated method able to classify the spontaneous spoken production of the subjects. In particular, we trained an artificial neural network using the spectrogram of the audio signal, which is the visual representation of the speech of the subject. Moreover, to overcome the problem of the large amount of annotated data usually required for training deep learning models, we used a specific data augmentation approach that avoids distorting the original samples. We evaluated the proposed method using the English Pitt Corpus from DementiaBank. The used dataset consists of 180 subjects: 43 healthy controls and 137 Alzheimer’s disease patients. The proposed method outperformed the other approaches in the literature based on manual and semi-automatic transcription and annotation of speech, improving the classification capability by 5.93%, and obtained good classification results compared to the state-of-the-art neuropsychological screening tests (i.e., the Mini-Mental State Examination and the Activities of Daily Living portion of the Blessed Dementia Rating Scale) exhibiting an accuracy of 93.30% and an F1 score of 88.50%.

An efficient context-aware screening system for Alzheimer's disease based on neuropsychology test

Alzheimer's disease (AD) and other dementias have become the fifth leading cause of death worldwide. Accurate early detection of the disease and its precursor, Mild Cognitive Impairment (MCI), is crucial to alleviate the burden on the healthcare system. While most of the existing work in the literature applied neural networks directly together with several data pre-processing techniques, we proposed in this paper a screening system that is to perform classification based on automatic processing of the transcripts of speeches from the subjects undertaking a neuropsychological test. Our system is also shown applicable to different datasets and languages, suggesting that our system holds a high potential to be deployed widely in hospitals across regions. We conducted comprehensive experiments on two different languages datasets, the Pitt dataset and the NTUHV dataset, to validate our study. The results showed that our proposed system significantly outperformed the previous works on both datasets, with the score of the area under the receiver operating characteristic curve (AUROC) of classifying AD and healthy control (HC) being as high as 0.92 on the Pitt dataset and 0.97 on the NTUHV dataset. The performance on classifying MCI and HC remained promising, with the AUROC being 0.83 on the Pitt dataset and 0.88 on the NTUHV dataset.

An Evaluation of Expedited Transcription Methods for School-Age Children's Narrative Language: Automatic Speech Recognition and Real-Time Transcription.

Purpose This study examined the accuracy and potential clinical utility of two expedited transcription methods for narrative language samples elicited from school-age children (7;5-11;10 [years;months]) with developmental language disorder. Transcription methods included real-time transcription produced by speech-language pathologists (SLPs) and trained transcribers (TTs) as well as Google Cloud Speech automatic speech recognition. Method The accuracy of each transcription method was evaluated against a gold-standard reference corpus. Clinical utility was examined by determining the reliability of scores calculated from the transcripts produced by each method on several language sample analysis (LSA) measures. Participants included seven certified SLPs and seven TTs. Each participant was asked to produce a set of six transcripts in real time, out of a total 42 language samples. The same 42 samples were transcribed using Google Cloud Speech. Transcription accuracy was evaluated through word error rate. Reliability of LSA scores was determined using correlation analysis. Results Results indicated that Google Cloud Speech was significantly more accurate than real-time transcription in transcribing narrative samples and was not impacted by speech rate of the narrator. In contrast, SLP and TT transcription accuracy decreased as a function of increasing speech rate. LSA metrics generated from Google Cloud Speech transcripts were also more reliably calculated. Conclusions Automatic speech recognition showed greater accuracy and clinical utility as an expedited transcription method than real-time transcription. Though there is room for improvement in the accuracy of speech recognition for the purpose of clinical transcription, it produced highly reliable scores on several commonly used LSA metrics. Supplemental Material https://doi.org/10.23641/asha.15167355.

Towards better subtitles: A multilingual approach for punctuation restoration of speech transcripts

This paper proposes a flexible approach for punctuation prediction that can be used to produce state-of-the-art results in a multilingual scenario. We have performed experiments using transcripts of TED Talks from the IWSLT 2017 and IWSLT 2011 evaluation campaigns. Our experiments show that the recognition errors of the ASR output degrade the performance of our models, in line with related literature. Our monolingual models perform consistently in Human-edited transcripts of German, Dutch, Portuguese and Romanian, suggesting that commas may be more difficult to predict than periods, using pre-trained contextual models. We have trained a single multilingual model that predicts punctuation in multiple languages that achieves results comparable with the ones achieved by monolingual models, revealing evidence of the potential of using a single multilingual model to solve the task for multiple languages. Then, we argue that usage of current punctuation systems in the literature are implicitly dependent on correct segmentation of ASR outputs for they rely on positional information to solve the punctuation task. This is too big of a requirement for use in a real life application. Through several experiments, we show that our method to train and test models is more robust to different segmentation. These contributions are of particular importance in our multilingual pipeline, since they avoid training a different model for each of the involved languages, and they guarantee that the model will be more robust to incorrect segmentation of the ASR outputs in comparison with other methods in the literature. To the best of our knowledge, we report the first experiments using a single multilingual model for punctuation restoration in multiple languages.

Automated Scoring of Tablet-Administered Expressive Language Tests.

Speech and language impairments are common pediatric conditions, with as many as 10% of children experiencing one or both at some point during development. Expressive language disorders in particular often go undiagnosed, underscoring the immediate need for assessments of expressive language that can be administered and scored reliably and objectively. In this paper, we present a set of highly accurate computational models for automatically scoring several common expressive language tasks. In our assessment framework, instructions and stimuli are presented to the child on a tablet computer, which records the child's responses in real time, while a clinician controls the pace and presentation of the tasks using a second tablet. The recorded responses for four distinct expressive language tasks (expressive vocabulary, word structure, recalling sentences, and formulated sentences) are then scored using traditional paper-and-pencil scoring and using machine learning methods relying on a deep neural network-based language representation model. All four tasks can be scored automatically from both clean and verbatim speech transcripts with very high accuracy at the item level (83−99%). In addition, these automated scores correlate strongly and significantly (ρ = 0.76–0.99, p < 0.001) with manual item-level, raw, and scaled scores. These results point to the utility and potential of automated computationally-driven methods of both administering and scoring expressive language tasks for pediatric developmental language evaluation.

#3079 Investigating the feasibility of automating the differential diagnosis of transient loss of consciousness

BackgroundThere are three common causes of Transient Loss of Consciousness (TLOC), syncope, epileptic and psychogenic nonepileptic seizures (PNES). Many individuals who have experienced TLOC initially receive an incorrect diagnosis and inappropriate treatment. Whereas syncope can be distinguished from the other two causes relatively easily with a small number of yes/no questions, the differentiation of the other two causes of TLOC is more challenging. Previous qualitative research based on the methodology of Conversation Analysis has demonstrated that epileptic and nonepileptic seizures are described differently when patients talk to clinicians about their TLOC experiences. One particularly prominent difference is that epileptic seizure descriptions are characterised by more formulation effort than accounts of nonepileptic seizures.AimThis research investigates whether features likely to reflect the level of formulation effort can be automatically elicited from audio recordings and transcripts of speech and used to differentiate between epileptic and nonepileptic seizures.MethodVerbatim transcripts of conversations between patients and neurologists were manually produced from video and audio recordings of interactions with 45 patients (21 epilepsy and24 PNES). The subsection of each transcript containing the patients account of their first seizure was manually extracted for the analysis. Seven automatically detectable features were designed as markers of formulation effort. These features were used to train a Random Forest machine learning classifier.ResultsThere were significantly more hesitations and repetitions in descriptions of first epileptic than nonepileptic seizures. Using a nested leave-one-out cross validation approach, 71% of seizures were correctly classified by the Random Forest classifier.ConclusionsThis pilot study provides proof of principle that linguistic features that have been automatically extracted from audio recordings and transcripts could be used to distinguish between epileptic seizures and PNES and thereby contribute to the differential diagnosis of TLOC. Future research should explore whether additional observations can be incorporated into a diagnostic stratification tool. Moreover, future research should explore the performance of these features when they have been extracted from transcripts produced by automatic speech recognition and when they are combined with additional information provided by patients and witnesses about seizure manifestations and medical history.

Converting raw transcripts into an annotated and turn-aligned TEI-XML corpus: the example of the Corpus of Serbian Forms of Address

This paper describes the procedure of building a TEI-XML corpus of spoken Serbian starting from raw transcripts. The corpus consists of semi–structured interviews, which were gathered with the aim of investigating forms of address in Serbian. The interviews were thoroughly transcribed according to GAT transcribing conventions. However, the transcription was carried out without tools that would control the validity of the GAT syntax, or align the transcript with the audio records. In order to offer this resource to a broader audience, we resolved the inconsistencies in the original transcripts, normalised the semi-orthographic transcriptions and converted the corpus into a TEI-format for transcriptions of speech. Further, we enriched the corpus by tagging and lemmatising the data. Lastly, we aligned the corpus turns to the corresponding audio segments by using a force-alignment tool. In addition to presenting the main steps involved in converting the corpus to the XML-format, this paper also discusses current challenges in the processing of spoken data, and the implications of data re-use regarding transcriptions of speech. This corpus can be used for studying Serbian from the perspective of interactional linguistics, for investigating morphosyntax, grammar, lexicon and phonetics of spoken Serbian, for studying disfluencies, as well as for testing models for automatic speech recognition and forced alignment. The corpus is freely available for research purposes.

“Put the what, where? Cut here?!” challenges to coordinating attention in robot-assisted surgery: a microanalytic pilot study

IntroductionDuring robot-assisted surgery (RAS), changes to the operating room configuration pose challenges to communication by limiting team members’ ability to see one another or use gesture. Referencing (the act of...

MOOD TYPES OF DONALD TRUMP’S SPEECH IN THE ARAB ISLAMIC AMERICAN SUMMIT

This research deals with analyzing language from the perspective of Systemic Functional Linguistics (SFL) from Donald Trump’s speech in the Arab Islamic American Summit. In English, one of types of meaning is Interpersonal meaning. It is realized by mood and modality. This research focuses on mood, then formulated into two research questions: 1) What mood types are used in Donald Trump’s speech in the Arab Islamic American summit? 2) What mood adjuncts are used in Donald Trump’s speech in the Arab Islamic American Summit?. The method used in this research is qualitative research. The source of data is the video of Donald Trump’s speech from Youtube. It was taken from (CNN, 2017) to get the transcript of speech by Donald Trump. It was found that there were 355 clauses in Donald Trump’s speech; there were 4 types of mood in the speech of Donald Trump; there were 337 declaratives mood with percentage 94.92%, 4 Polar-Interrogatives mood with percentage 1.12%, 4 WH-Interrogatives mood with percentage 1.12% and 10 imperatives mood with percentage 2.25%. Declarative as the most dominant types of mood is used in Donald Trump’s, speech which means that he gave or stated information to the audiences. In addition, in Donald Trump’s speech, there are 19 clauses which contain mood adjunct. The findings indicated that there were 3 types of mood adjunct in the speech, namely, 5 adjuncts of modality with percentage 26.31%, 3 adjuncts of temporality with percentage 15.78%, and 11 adjuncts of intensity with percentage 57.89%. The most dominant is adjuncts of intensity which expresses expectation. Keywords: Systemic Functional Linguistics; Mood; Mood Adjunct