Context Of Speech Research Articles

Non-plural interpretations of some: Mouse-tracking evidence for quick social reasoning in real-time

In support of an account in which disfluency can cue social reasoning in real time, Loy et al. (2019) showed that listeners are more likely to make an early commitment to a socially undesirable meaning of some as all, if it follows disfluent uh in a context where larger values are associated with greed (“I ate, [uh], some biscuits”). However, their finding is also compatible with an account in which disfluency simply heightens attention to the core semantic meaning of some, namely, some and possibly all. The current study differentiates these two accounts, using contexts in which smaller values are the socially undesirable interpretations of some. In two experiments, we recorded participants’ mouse movements as they heard fluent and disfluent utterances in a job interview context (“I have, [uh], some qualifications”) and clicked on one of four images corresponding to specific interpretations of some. Here, in keeping with an account in which the effects of disfluency reflect social reasoning, and contrary to one in which such effects depend on heightened attention, disfluency reduces the value participants associate with some. We found that participants were more likely to select images corresponding to one, or zero, qualifications following disfluent utterances. However, their mouse movements show that they are quick to commit to one qualification (Experiment 1) and slow to commit to zero (Experiment 2), suggesting that social context and manner of speech can combine to affect the interpretation of some as an utterance unfolds. Assigning its meaning to one is relatively easy, but imposing a meaning of zero – in effect, deciding that a speaker is lying – is more demanding.

Prosodic variation between contexts in infant-directed speech.

Speakers consider their listeners and adjust the way they communicate. One well-studied example is the register of infant-directed speech (IDS), which differs acoustically from speech directed to adults. However, little work has explored how parents adjust speech to infants across different contexts. This is important because infants and parents engage in many activities throughout each day. The current study tests whether the properties of IDS in English vary across three in-lab tasks (sorting objects, free play, and storytelling). We analysed acoustic features associated with prosody, including mean fundamental frequency (F0, perceived as pitch), F0 range, and word rate. We found that both parents' pitch ranges and word rates varied depending on the task in IDS. The storytelling task stood out among the tasks for having a wider pitch range and faster word rate. The results depict how context can drive parents' speech adjustments to infants.

More than just a happy talk? Evidence for functional pitch and utterance length modifications in infant-, spouse-, and dog-directed communication.

By comparing infant-directed speech to spouse- and dog-directed talk, we aimed to investigate how pitch and utterance length are modulated by speakers considering the speech context and the partner's expected needs and capabilities. We found that mean pitch was modulated in line with the partner's attentional needs, while pitch range and utterance length were modulated according to the partner's expected linguistic competence. In a situation with a nursery rhyme, speakers used the highest pitch and widest pitch range with all partners suggesting that infant-directed context greatly influences these acoustic features. Recent findings showed that these speakers expressed more intense positive emotions towards their infants and spouses than towards their dogs. Our results revealed different patterns, leading us to conclude that these acoustic features are not simple by-products of emotional speech. Instead, they are dynamically and functionally used in accordance with the speech context and the audience's expected needs and capabilities.

The Dog Who Tells Lies in Nikolai Gogol’s Comedy “Marriage”

This article draws attention to the expression pyos vryot ‘dog tells lies’ found in the matchmaker’s line from the comedy “Marriage” by Nikolai Gogol. Researchers have already studied the peculiarities of women’s speech in this comedy and the “linguistics of lying” in Gogol’s texts but the phrase “lying dog” was hardly noticed by them. The word dog addressed to a person have long been considered an obscene word which correlated with the disdainful attitude towards these animals. The East Slavic word brekhat’ possesses two meanings ‘to bark’ and ‘to tell lies’. The main meaning of brekhat’ in Russian is ‘to bark’ while the meaning ‘to lie’ is more common in Russian dialects. On the contrary, the main meaning of Ukrainian brekhati is ‘to tell lies’. The action of the comedy “Marriage” was originally supposed to take place in a village or small town (apparently in Ukraine). Gogol worked on the comedy for a long time and later moved its action to St. Petersburg. While working on his comedy, Gogol didn’t delete or rework this line although the expression pyos vryot is either a Ukrainianism or a colloquial expression, which is not very appropriate in the speech context of the Russian metropolitan society. This expression is not noted among the identified Ukrainianisms in Gogol’s texts. Perhaps Gogol deliberately retained such unusual words for the matchmaker showing that while living in St. Petersburg she remains a provincial.

Between role and expression: Conjunction analysis and its pedagogical implication in teaching writing

This qualitative study used a combination of linguistic and rhetorical analysis to look at how conjunctions are used in Donald Trump and Joe Biden's victory speeches. Grounded in a Systemic Functional Linguistics (SFL) theory, this article examined the functional features of language in the social context of political speech. The result of analysis shows distinctive patterns in conjunction choices that include locative, additive, contrastive, purpose-driven, simultaneous, sequential, conditional, and causal roles. While Trump's speech stresses additive and simultaneous structures, reflecting his dynamic and emotive rhetorical approach, Biden's speech lens toward purposeful conjunction, in line with a policy-oriented communication style. In the meantime, some types of conjunctions are absent prompts a reflection on implicit messages. Overall, by raising students' awareness of conjunction usage and its rhetorical impact in persuasive discourse, this study offers a nuanced exploration of language choices and advances our understanding of how conjunctions function within the larger context of political communication. This has potential pedagogical implications for ELT, particularly in the teaching of writing.

Discourse Marker Well in British and Hong Kong English: A Comparative Analysis of Scripted and Unscripted Speech

Abstract: This study offers a comparative analysis of the discourse marker well in both British English (BrE) and Hong Kong English (HKE), focusing on its usage in scripted and unscripted speech contexts. Using Beeching's classification framework, 170 instances of well were analyzed to investigate its pragmatic functions, such as signaling hesitation, managing transitions, and structuring discourse. The analysis reveals that well is used more frequently in unscripted speech in both varieties, although BrE speakers tend to use it more for topic transitions, while HKE speakers employ it more frequently to express hesitation. These findings suggest that while well serves universal pragmatic functions, its use is influenced by cultural and linguistic factors in different English varieties. The study also highlights the significance of discourse markers like well in maintaining coherence in conversation, with important implications for teaching English as a second language, especially in multilingual and diverse regions such as Hong Kong.

Deep-learning models reveal how context and listener attention shape electrophysiological correlates of speech-to-language transformation.

To transform continuous speech into words, the human brain must resolve variability across utterances in intonation, speech rate, volume, accents and so on. A promising approach to explaining this process has been to model electroencephalogram (EEG) recordings of brain responses to speech. Contemporary models typically invoke context invariant speech categories (e.g. phonemes) as an intermediary representational stage between sounds and words. However, such models may not capture the complete picture because they do not model the brain mechanism that categorizes sounds and consequently may overlook associated neural representations. By providing end-to-end accounts of speech-to-text transformation, new deep-learning systems could enable more complete brain models. We model EEG recordings of audiobook comprehension with the deep-learning speech recognition system Whisper. We find that (1) Whisper provides a self-contained EEG model of an intermediary representational stage that reflects elements of prelexical and lexical representation and prediction; (2) EEG modeling is more accurate when informed by 5-10s of speech context, which traditional context invariant categorical models do not encode; (3) Deep Whisper layers encoding linguistic structure were more accurate EEG models of selectively attended speech in two-speaker "cocktail party" listening conditions than early layers encoding acoustics. No such layer depth advantage was observed for unattended speech, consistent with a more superficial level of linguistic processing in the brain.

Reduplication; Noun Formation Pattern in Ternate Language a Morphophonemic Study

The Ternate language is one of the non-Austronesian languages which has several unique characteristics, including the form of prefixes and possessive verbs that can only be differentiated based on the context of speech and the absence of inflexion. It will produce or find a pattern of rules from the formation of nouns in the Ternate language. Data were analyzed morphophonemically using structural theory to find out the forms of nouns in Ternate and the process of their formation The main aim of this research is to find rules for noun formation, and patterns in the Ternate language. The method in this research is descriptive qualitative with observation and interview data collection techniques as well as literature study as data sources. Meanwhile, data analysis was carried out through the identification and classification stages as well as analysis of the formation process as nouns. In conclusion that nouns are formed from verb and adjective classes by repeating and adding phonemes; these nouns have the meanings of actor, tool, place/locative, possession, rather, and abstract.

A longitudinal investigation of the acoustic properties of infant-directed speech from 6 to 18 months.

Caregivers often modulate their speech when interacting with infants, adapting a register that has been suggested to have attentional, affective and didactic purposes. The present preregistered study examined the longitudinal trajectories of a diverse range of acoustic features of infant-directed speech (IDS) and compared these with adult-directed speech (ADS), in Norwegian parents of 6- to 18-month-old infants. Sixty-nine families participated. Throughout five laboratory visits across one year, parents were recorded reading a picture-book to their infant (IDS) and an experimenter (ADS). The book was designed to tightly control for the linguistic content and context of speech between participants, timepoints and registers. Analyses of a total of 54 594 vowels and 22 958 phrases revealed, first, an overall effect of register: parents used higher pitch, wider pitch range, slower articulation rate, longer vowel duration and more variable and less distinct vowels in IDS than in ADS. Second, significant register-by-age interactions indicated that parents' IDS, compared with their ADS, featured wider pitch range, larger vowel space and shorter vowel duration in older as compared with younger infants, while pitch, articulation rate and vowel variability and distinctiveness remained relatively stable with age. Results are discussed in the context of the proposed functions of IDS.

Graph Neural Network-Based Speech Emotion Recognition: A Fusion of Skip Graph Convolutional Networks and Graph Attention Networks

In speech emotion recognition (SER), our research addresses the critical challenges of capturing and evaluating node information and their complex interrelationships within speech data. We introduce Skip Graph Convolutional and Graph Attention Network (SkipGCNGAT), an innovative model that combines the strengths of skip graph convolutional networks (SkipGCNs) and graph attention networks (GATs) to address these challenges. SkipGCN incorporates skip connections, enhancing the flow of information across the network and mitigating issues such as vanishing gradients, while also facilitating deeper representation learning. Meanwhile, the GAT in the model assigns dynamic attention weights to neighboring nodes, allowing SkipGCNGAT to focus on both the most relevant local and global interactions within the speech data. This enables the model to capture subtle and complex dependencies between speech segments, thus facilitating a more accurate interpretation of emotional content. It overcomes the limitations of previous single-layer graph models, which were unable to effectively represent these intricate relationships across time and in different speech contexts. Additionally, by introducing a pre-pooling SkipGCN combination technique, we further enhance the ability of the model to integrate multi-layer information before pooling, improving its capacity to capture both spatial and temporal features in speech. Furthermore, we rigorously evaluated SkipGCNGAT on the IEMOCAP and MSP-IMPROV datasets, two benchmark datasets in SER. The results demonstrated that SkipGCNGAT consistently achieved state-of-the-art performance. These findings highlight the effectiveness of the proposed model in accurately recognizing emotions in speech, offering valuable insights and a solid foundation for future research on capturing complex relationships within speech signals for emotion recognition.

Vertical larynx actions and intergestural timing stability in Hausa ejectives and implosives.

The current project undertakes a kinematic examination of vertical larynx actions and intergestural timing stability within multi-gesture complex segments such as ejectives and implosives that may possess specific temporal goals critical to their articulatory realization. Using real-time MRI (rtMRI) speech production data from Hausa non-pulmonic and pulmonic consonants, this study illuminates speech timing between oral constriction and vertical larynx actions within segments and the role this intergestural timing plays in realizing phonological contrasts and processes in varying prosodic contexts. Results suggest that vertical larynx actions have greater magnitude in the production of ejectives compared to their pulmonic counterparts, but implosives and pulmonic consonants are differentiated not by vertical larynx magnitude but by the intergestural timing patterns between their oral and vertical larynx gestures. Moreover, intergestural timing stability/variability between oral and non-oral (vertical larynx) actions differ among ejectives, implosives, and pulmonic consonants, with ejectives having the most stable temporal lags, followed by implosives and pulmonic consonants, respectively. Lastly, the findings show how contrastive linguistic 'molecules'- here, segment-sized phonological complexes with multiple gestures- interact with phrasal context in speech in such a way that it variably shapes temporal organization between participating gestures as well as respecting stability in relative timing between such gestures comprising a segment.

Contrastive Learning based Speech Spoofing Detection for Multimedia Security in Edge Intelligence

Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this paper first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on the ASVspoof2019 dataset show that GEMINI reduces the Equal Error Rate and tandem Detection Cost Function by up to 96.75% and 96.35% in the physical access scenario, and by up to 86.62% and 87.71% in the logical access scenario compared to peer methods.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

Characteristics of Nasal Coarticulation by Nasality Visualization System in Children with Speech Sound Disorders

Objectives: In this study, we analyzed the nasal coarticulation characteristics of children with and without speech sound disorders by comparing nasalance scores according to speech contexts. Methods: The subjects of this study were 30 children, 15 children with speech sound disorder (SSD) and 15 typically developing (TD) children, aged 4 to 6 years old. Subjects were asked to speak stimuli in different speech contexts-low vs. high vowels, nasal consonant+vowel (NV) vs. vowel+nasal consonant (VN) syllables, words vs. nonwords - and nasalance scores were measured using the Nasality Visualization System (NVS) on their speech samples. Results: First, both groups showed significantly higher naslance scores for high vowels compared to low vowels, with a significant between-group difference in high vowel. Second, both groups showed significantly higher nasalance scores for VN syllables than for NV syllables, with no between-group difference. Third, both groups showed significantly lower nasalance scores in nonwords compared to words. While there was no group difference in the words, the nasalance scores of the SSD group was lower in the nonwords, which was significantly different from that of the TD groups. Conclusion: These results suggest that the SSD group exhibits somewhat different nasal characteristics than the TD group, and furthermore, that SSD children articulate phoneme sequences more segmentally in unlearned nonword contexts.

A Corpus Pragmatics Approach to Active Listenership in Conversations in British English: Contextualised Function-To-Form

Active Listenership are the features that refer to the linguistic behaviour of the listener in casual conversations. The listener continuously utters Listener Response Tokens, Sentence-Completions and Which-Comment-Clauses to signal emotional and social involvement in the discourse. Particularly in the field of corpus pragmatics, Active Listenership has found attention over the past two decades. In this paper, I introduce a novel context-driven function-to-form corpus pragmatics annotation scheme. The scheme is applied to a down-sampled version of the spoken BNC2014, and subsequently evaluated in terms of inter-rater plausibility. Then, I present the qualitative findings on how frequent the features of Active Listenership are in the corpus in general. Subsequently, I analyse the contextual units (as per my annotation scheme) to show which feature of Active Listenership is most likely to occur in a specific speech context. I finish this paper with suggestions for future research and a call to include more features of conversational English into an elaboration of the annotation scheme. My findings can be applied to general pragmatics research and English Language Teaching.

THE ANALYSIS OF THE SPEECH “WHAT ISLAM REALLY SAYS ABOUT WOMEN” BY ALAA MURABIT BASED ON THE PERSPECTIVE OF LINGUISTICS BEHAVIORISM

This study aims to explain the analysis of the meaning of a speech context delivered by Alaa Murabit in the speech "What Islam Really says about Women" on the TED YouTube channel (July 21, 2015) in order to help the audience in interpreting the context of meaning so as not to cause misunderstandings. This study uses the theory of Linguistic Behaviorism pioneered by Bloomfield (2013). The method used in analyzing data is a qualitative descriptive method by selecting some data in a speech that contains a mismatch of context with stimulus. The result of this study, Alaa Murabit, used two main stimuli: family background and religion, so that it gave rise to a response in the form of a parable sentence that has many interpretations.

العلاقة بين استيعاب المفردات وتحصيل الطالبات التعليمي في مهارة الكلام للصف الأول من المتوسط بمعهد عبد الله بن مسعود أساهان

Speaking skill is a productive skill that involves the process of learning various aspects of the Arabic language. To acquire this skill, one needs to understand grammar, morphology, vocabulary, style, and meanings. Proficiency in speaking Arabic requires comprehension of the elements and rules of the language, including vocabulary acquisition, sentence structure, speech context, and the ability to understand the speaker's language. Vocabulary is a very important linguistic element that must be mastered, used in both spoken and written language. This issue leads to weaknesses in vocabulary comprehension and the presence of excuses for not speaking Arabic in certain situations. The methodology used in this research is a quantitative correlational method, and the data analysis technique employed is the statistical method using Pearson's product-moment correlation coefficient. The research results indicate that there is a moderate positive correlation between vocabulary comprehension and students' academic achievement in speaking skills for the first-grade students at the Abdullah bin Masoud Institute in Asahan, with a correlation coefficient value of 0.474.

A Pragmatic Study: Investigating The Meaning of Social Deixis in Spoken Discourse

This research aims to investigate the types of social deixis and analyze their social meaning in a speech context. This research used a qualitative method. The participant of this research is Retno Marsudi’s utterances, who the Ministry of Foreign Affairs of the Republic of Indonesia. The data was collected from YouTube and a transcript from the Ministry of Foreign Affairs of the Republic of Indonesia’s website. The data collected was analyzed using qualitative analysis, where the data was divided into five types of deixis and then narrowed down into two types of social deixis. After the data narrowed down, the researchers analyzed the meaning of the social deixis based on contextual and sociocultural theory. The results of the analysis conclude that there are two types of social deixis: 1) relational social deixis and 2) place social deixis with three data of relational and one data of place social deixis. The social meaning of social deixis imply profesionalism, social status, familiarity, and formality.

Strategy for developing a speech recognition model specialized for patients with depression or Parkinson's disease with small size speech database.

Most of speech recognition models currently in use have been dealt with speech of normal people. The speech recognition rate for patients with depression or Parkinson's disease (PD) who show differences in speech characteristics compared to normal subjects is lower than that of normal subjects. This study explores the model to enhance accuracy of speech recognition for individuals who have depression or PD, aiming to provide them more accurate service. In this study, considering the speech features of patients with depression or PD, we designed a model with the assumption that understanding the overall meaning and context of speech through the utilization of global information, rather than local information, is more effective in enhancing recognition accuracy. We propose the m-Globalformer, a model based on the Globalformer architecture that combines the squeeze-and-excitation (SE) module with the Transformer. The m-Globalformer enhances the utilization of global information by modifying the base SE module. The model employs pre-training and fine-tuning strategies, considering the limited speech data of the patients. In the initial training phase, a large-scale normal speech dataset was used, followed by fine-tuning the model with a small-scale dataset of depression or PD patients. The m-Globalformer demonstrated superior performance in our experiments, achieved character error rates (CER) of 11.28% for depression and 19.67% for PD.

An Ideational Metafunction Analysis of Indonesian Presidential Candidate Speeches

The problem in this research is what the discourse context and inferences are about the speeches of the Indonesian presidential candidates in 2024. The focus is on discussing Prabowo-Gibran's expressions in their orations. The aim of the research is to describe the context and inference of discourse Prabowo Subianto's speech on electronic media TEMPO.CO and CNBC Indonesia. The type of research is qualitative research using a descriptive approach. The data sources in this research were taken from electronic media Tempo.CO and CNBC Indonesia.The data analysis technique uses content analysis. The results of context research and discourse inference in Prabowo Subianto's speech are that Prabowo Subianto's speech as a whole contains the context and inference of campaigning, enthusiasm, readiness and optimism of the presidential candidate pair. and representatives, the winning team, supporters, and especially the entire Gerindra Party family, with persuasive speeches for get more support so you can achieve victory. Prabowo's expressions regarding his work program are still being monitored and are being questioned by the public.