Neonatal cry signal prediction and classification via dense convolution neural network

The study of neonatal cry signals is always an interesting topic and still researcher works interminably to develop some module to predict the actual reason for the baby cry. It is really hard to predict the reason for their cry. The main focus of this paper is to develop a Dense Convolution Neural network (DCNN) to predict the cry. The target cry signal is categorized into five class based on their sound as “Eair”, “Eh”, “Neh”, “Heh” and “Owh”. Prediction of these signals helps in the detection of infant cry reason. The audio and speech features (AS Features) were exacted using Mel-Bark frequency cepstral coefficient from the spectrogram cry signal and fed into DCNN network. The systematic DCNN architecture is modelled with modified activation layer to classify the cry signal. The cry signal is collected in different growth phase of the infants and tested in proposed DCNN architecture. The performance of the system is calculated through parameters accuracy, specificity and sensitivity are calculated. The output of proposed system yielded a balanced accuracy of 92.31%. The highest accuracy level 95.31%, highest specificity level 94.58% and highest sensitivity level 93% attain through proposed technique. From this study, it is concluded that the proposed technique is more efficient in detecting cry signal compared to the existing techniques.

Figure of Speech Stylistic Analisys on Song Liryc in Sami Yusuf’s Albums

This research aimed to analyze figure of speech Stylistic in Sami Yusuf’s song lyrics. The author is intrigued by the songwriter's lyrics that may occur in the life we live. The purpose of this study is to determine whether or not there are any figure of speech features in Sami Yusuf's song lyrics, as well as to examine the meaning of the song lyrics found in Sami Yusuf's song lyrics. Content analysis was a part of this study. The method used in this study is descriptive qualitative research, and the design is qualitative research. The data was gathered through library research. The researcher is the research instrument since he or she is the main instrument in this study who collects and evaluates the data. After finishing the investigation, the researcher discovered that the most common figure of speech is personification, which has 14 data, and the least common figure of speech is paradox, which has one data. Metaphor (4 data), simile (7 data), synecdoche (10 data), metonymy (5 data), symbol (4 data), and hyperbole (5 data) are some of the other data collected. In other cases, the researcher found no figures of speech like allegory, apostrophe, understatement, or linguistic irony. The connotations of figure of speech utilized in Sami Yusuf's song lyrics are varied. Keywords: figure of speech, song lyric, stylistic

Linguistic Speech as a Challenge for the Translator and the Foreign Language Tuition

The current article examines Bulgarian public political speech as a challenge for the professional translators and as a series of political messages addressed to young people who study at higher educational institutions in Bulgaria. The article analyses the perception of clichéd constructions as well as the models of speech aggression which are widely practiced in different discussion formats such as election campaign debates, parliamentary control, public debates on TV where important public issues are discussed. The observations on the presidential election campaign in 2021 are also presented as well as the perception of the political messages by the nominees. The main conclusion from the scientific study shows that both Bulgarians and foreigners face problems with the grammatically correct sentence constructions behind which there is a minimum of information. The study also confirms the hypothesis that foreign students in Bulgaria recognize a number of common features between the political speech in our country and in their home countries. The clichés and the speech aggression are present in the global political speech as a component which defines the models of public speech and Bulgaria is no exception from that. A similar conclusion can be made about the interconnection between political and journalistic speech where the following common speech features can be recognized: inability to listen to the interlocutor; politicians slide over journalistic questions; the “self” overexposure, etc.

Can online translators and their speech capabilities help English learners improve their pronunciation?

This study investigated whether a translation tool (Microsoft Translator – MT) and its built-in speech features (Text-To-Speech synthesis – TTS – and speech recognition) can promote learners’ acquisition in pronunciation of English regular past tense -ed in a self-directed manner. Following a pretest/posttest design, we compared 29 participants’ performances of past -ed allomorphy (/t/, /d/, and /id/) by assessing their pronunciation in terms of phonological awareness, phonemic discrimination, and oral production. The findings highlight the affordances of MT regarding its pedagogical use for helping English as a Foreign Language (EFL) learners improve their pronunciation.

Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions

Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored. In this study, we developed a tablet-based application and collected speech responses of 57 older adults to daily life questions regarding, for example, one's feelings and future travel plans. From audio data of these speech responses, we automatically extracted speech features characterizing acoustic, prosodic, and linguistic aspects, and investigated their associations with self-rated scores of the UCLA Loneliness Scale. Consequently, we found that with increasing loneliness scores, speech responses tended to have less inflections, longer pauses, reduced second formant frequencies, reduced variances of the speech spectrum, more filler words, and fewer positive words. The cross-validation results showed that regression and binary-classification models using speech features could estimate loneliness scores with an R2 of 0.57 and detect individuals with high loneliness scores with 95.6% accuracy, respectively. Our study provides the first empirical results suggesting the possibility of using speech data that can be collected in everyday life for the automatic assessments of loneliness in older adults, which could help develop monitoring technologies for early detection and intervention for mitigating loneliness.

Stress Level Detection and Evaluation from Phonation and PPG Signals Recorded in an Open-Air MRI Device

This paper deals with two modalities for stress detection and evaluation—vowel phonation speech signal and photo-plethysmography (PPG) signal. The main measurement is carried out in four phases representing different stress conditions for the tested person. The first and last phases are realized in laboratory conditions. The PPG and phonation signals are recorded inside the magnetic resonance imaging scanner working with a weak magnetic field up to 0.2 T in a silent state and/or with a running scan sequence during the middle two phases. From the recorded phonation signal, different speech features are determined for statistical analysis and evaluation by the Gaussian mixture models (GMM) classifier. A database of affective sounds and two databases of emotional speech were used for GMM creation and training. The second part of the developed method gives comparison of results obtained from the statistical description of the sensed PPG wave together with the determined heart rate and Oliva–Roztocil index values. The fusion of results obtained from both modalities gives the final stress level. The performed experiments confirm our working assumption that a fusion of both types of analysis is usable for this task—the final stress level values give better results than the speech or PPG signals alone.

Artistic Image Depiction Elements in the Works of Edgar Allan Poe

The article examines the elements of the representation of the artistic image in a literary work, individual speech features, phonetic devices, morphological categories and expressions of the character's speech. The literary device of stanzas of Edgar Allan's works is analyzed and examined, the semantic features of the features of images and all kinds of reproduced linguistic means are traced. Keywords: reproducible linguistic means, element of artistic image, phonetic devices, individuality, expressiveness, versatility, vivid and expressive meaning, disgusting abomination, deep feeling of loneliness, personification, inanimate objects, human qualities.