Paralinguistic Information Research Articles

A Novel Hybrid Attention-Based Dilated Network for Depression Classification Model from Multimodal Data Using Improved Heuristic Approach

Automatic depression classification from multimodal input data is a challenging task. Modern methods use paralinguistic information such as audio and video signals. Using linguistic information such as speech signals and text data for depression classification is a complicated task in deep learning models. Best audio and video features are built to produce a dependable depression classification system. Textual signals related to depression classification are analyzed using text-based content data. Moreover, to increase the achievements of the depression classification system, audio, visual, and text descriptors are used. So, a deep learning-based depression classification model is developed to detect the person with depression from multimodal data. The EEG signal, Speech signal, video, and text are gathered from standard databases. Four stages of feature extraction take place. In the first stage, the features from the decomposed EEG signals are attained by the empirical mode decomposition (EMD) method, and features are extracted by means of linear and nonlinear feature extraction. In the second stage, the spectral features of the speech signals from the Mel-frequency cepstral coefficients (MFCC) are extracted. In the third stage, the facial texture features from the input video are extracted. In the fourth stage of feature extraction, the input text data are pre-processed, and from the pre-processed data, the textual features are extracted by using the Transformer Net. All four sets of features are optimally selected and combined with the optimal weights to get the weighted fused features using the enhanced mountaineering team-based optimization algorithm (EMTOA). The optimal weighted fused features are finally given to the hybrid attention-based dilated network (HADN). The HDAN is developed by combining temporal convolutional network (TCN) with bidirectional long short-term memory (Bi-LSTM). The parameters in the HDAN are optimized with the assistance of the developed EMTOA algorithm. At last, the classified output of depression is obtained from the HDAN. The efficiency of the developed deep learning HDAN is validated by comparing it with various traditional classification models.

Research Paper on Emotion Recognition

As a recognizing in machine learning algorithm a significant amount of in different various field has been done in many technologies field of machines through which speech has a major impact research interest, especially in the affective computing domain. Increasing potential, algorithmic advancements, and applications in real-world. This human speech contains para-linguistic information that can be represented using different various quantitative features such as pitch, intensity for its deltaic result. It is commonly achieved following three key steps: data processing, feature extraction, and classification based on the underlying emotional features. The nature of these steps, help with the distinct features of human speech, to get the exact result through the underpin with the use of ML methods. Many techniques have been utilized to extract emotions from signals, including many well-established speech analysis and classification techniques. Emotion recognition the review covers databases used, emotions extracted, contributions made toward emotion recognition and limitations related to it. signals are an important but challenging component of Human-Computer Interaction (HCI) in machine learning aspect in computer machines through various different perspective and given signals

MBCFNet: A Multimodal Brain–Computer Fusion Network for human intention recognition

Accurate recognition of human intent is crucial for effective human–computer speech interaction. Numerous intent understanding studies were based on speech-to-text transcription, which often overlook the influence of paralinguistic cues (such as speaker’s emotion, attitude, etc.), leading to misunderstandings, especially when identical textual content conveys multiple intents by means of different paralinguistic information. Considering that interaction intent is produced from the human brain, we propose a novel Multimodal Brain–Computer Fusion Network (MBCFNet) to discriminate the different intents carried by the identical textual information, in which the acoustic-textual representation was adapted by brain functional information through a cross-modal transformer. In the output module, a joint multi-task learning method was used to optimize both the primary task of intent recognition and the auxiliary task of emotion recognition of the speaker. To evaluate the model performance, we constructed a multimodal dataset CMSLIU that consists of acoustic, textual, and electroencephalograph (EEG) data of the same Chinese texts with varying intents. Experimental results on the self-constructed dataset indicate that the proposed model achieves state-of-the-art (SOTA) performance compared with other competing models. Ablation experiment results reveal that the model performance declines without information from EEG or audio modality, and also when the auxiliary task of emotion recognition is removed. All these results suggest the effectiveness of the proposed information fusion strategy in spoken language intent recognition and the brain–computer information fusion idea can also be extended to other similar fields.

Аналитический обзор методов автоматического анализа экстралингвистических компонентов спонтанной речи

The accuracy of automatic spontaneous speech recognition systems is far from that of trained speech recognition systems. This is due to the fact that spontaneous speech is not as smooth and failure-free as spontaneous speech. Spontaneous speech varies from speaker to speaker: the quality of phonemes’ pronunciation, the presence of pauses, speech disruptions and extralinguistic items (laughing, coughing, sneezing, and chuckling when expressing emotions of irritation, etc.) interrupt the fluency of verbal speech. However, it is worth noting that extralinguistic items very often carry important paralinguistic information, so it is crucial for automatic spontaneous speech recognition systems not only to identify such phenomena and distinguish them from the verbal components of speech but also to classify them. This review presents an analysis of works on the topic of automatic detection and analysis of extralinguistic items in spontaneous speech. Both individual methods and approaches to the recognition of extralinguistic items in a speech stream, and works related to the multiclass classification of isolatedly recorded extralinguistic units are considered and described. The most popular methods of extralinguistic units’ analysis are neural networks, such as deep neural networks and networks based on transformer models. The basic concepts related to the term extralinguistic items are given, the original systematization of extralinguistic items in the Russian language is proposed, the corpus and databases of audio spoken speech both in Russian and in other languages are described, the data sets of extralinguistic items recorded isolatedly are also given. The accuracy of extralinguistic items recognition increases with the following conditions of work with the speech signal: pre-processing of audio signals of items has shown an increase in the accuracy of separately recorded extralinguistic items classification; consideration of context (analysis of several frames of speech signal) and use of filters for smoothing the time series after extraction of feature vectors showed an increase in accuracy in frame-by-frame analysis of the speech signal with spontaneous speech.

Обучение иностранных студентов пересказу на основе кинотекста (в аспекте русского языка как иностранного)

The aim of the study is to substantiate the need and possibility of using the method of retelling film texts in one’s own words as one of the means of mastering oral monologue speech in the practical course of Russian as a foreign language at an advanced stage of training. The paper provides rationale for using the method of retelling based on authentic feature films, describes the content of teaching retelling by considering all its components, presents a set of practical tasks aimed at developing the skills and abilities of processing the original text by reduction, simplification and expansion. The scientific novelty of the study lies in the following: it is the first time that the content of teaching foreign students how to retell based on film texts in the Russian language course has been clarified, the specifics of interpreting linguistic and non-linguistic components of the film text in secondary oral text production has been revealed, which made it possible to develop a set of exercises aimed at forming the speech skills and abilities necessary for the construction of a narrative-descriptive monologue. As a result of the study, it was found that teaching retelling based on films is directly linked to the work on reconstructing a text perceived by ear taking into account the presented paralinguistic information and the formation of the corresponding skills of organizing the content and choosing linguistic means of narration/description, namely, the skills of conveying the statements said by other people and using text compression means.

Justice must be seen to be done: a multimodal attitude analysis of attorneys’ closing arguments

Abstract Multimodal discourse analysis offers a novel lens for the study of legal discourse. Within the closing arguments of a trial, prosecution and defense attorneys utilize various multimodal resources to present evidence, articulate opinions, and adopt stances to achieve their communicative goals. This research focuses on the closing arguments in the criminal trial concerning the death of George Floyd to analyze the multimodal representation of the closing arguments delivered by both prosecution and defense attorneys. It employs the analytical framework of the attitude system and paralinguistic typology proposed by Martin and colleagues, and uses the UAM Corpus Tool and Praat to qualitatively annotate and quantify the linguistic and paralinguistic information. The findings reveal that these attorneys employ different multimodal resources, including language and paralanguage, to convey their attitudes towards the case’s participants. This leads to the construction of competing narratives, shedding light on how justice is seen to be done.

Subtitling for people with hearing impairments in the Arab world context: The case of <i>the Blue Elephant 2</i> movie

Although technology advancements have increased the availability of screens everywhere, making it possible to watch AV products anytime, the deaf-and-hard-of-hearing (DHH) accessibility to these materials in the Arab world is still poor. Only a few Arabic movies with this feature are available via Netflix. In this study, the subtitles for the deaf and hard of hearing (SDH) in <i>The Blue Elephant 2</i> movie are analyzed. This study qualitatively examines the type of information covered in the subtitles of the investigated Arabic movie for Arab viewers with hearing impairment. It also quantitatively investigates the reactions of people with hearing impairment to the technical aspects of the subtitles using a structured questionnaire, which consisted of five open-ended questions. The qualitative analysis revealed two main types of information, namely, para-linguistic/extra-linguistic and linguistic. The para-linguistic/extra-linguistic aspect includes information about music, speakers, vocal non-linguistic features, and non-verbal signs. The linguistic aspect is related to the code-switching between languages where the speaker may use some languages other than Arabic. The quantitative analysis showed that the participants reacted positively to the technical aspects of the subtitles, including the length and synchrony. They stated that they had enough time to read and process the information included in the subtitles. The paralinguistic information was reflected in the subtitles and helped them better understand the movie. The sample attitude towards SDH was good and positive, and they like to watch more Arabic movies with this feature in the future. This study concludes that more attention to DHH people in terms of accessibility to audiovisual content should be paid. This will likely improve their linguistic and cultural skills and help them integrate more with the surrounding community. The study’s findings have substantial implications for enhancing DHH individuals’ access to audiovisual content in Jordan and the wider Arab region. The study emphasizes the need to deliver high-quality SDH that incorporates both linguistic and paralinguistic information to DHH viewers in order to improve their viewing experience.

ACOUSTICALLY-DRIVEN PHONEME REMOVAL THAT PRESERVES VOCAL AFFECT CUES.

In this paper, we propose a method for removing linguistic information from speech for the purpose of isolating paralinguistic indicators of affect. The immediate utility of this method lies in clinical tests of sensitivity to vocal affect that are not confounded by language, which is impaired in a variety of clinical populations. The method is based on simultaneous recordings of speech audio and electroglotto-graphic (EGG) signals. The speech audio signal is used to estimate the average vocal tract filter response and amplitude envelop. The EGG signal supplies a direct correlate of voice source activity that is mostly independent of phonetic articulation. These signals are used to create a third signal designed to capture as much paralinguistic information from the vocal production system as possible-maximizing the retention of bioacoustic cues to affect-while eliminating phonetic cues to verbal meaning. To evaluate the success of this method, we studied the perception of corresponding speech audio and transformed EGG signals in an affect rating experiment with online listeners. The results show a high degree of similarity in the perceived affect of matched signals, indicating that our method is effective.

Paralinguistic Privacy Protection at the Edge

Voice user interfaces and digital assistants are rapidly entering our lives and becoming singular touch points spanning our devices. These always-on services capture and transmit our audio data to powerful cloud services for further processing and subsequent actions. Our voices and raw audio signals collected through these devices contain a host of sensitive paralinguistic information that is transmitted to service providers regardless of deliberate or false triggers. As our emotional patterns and sensitive attributes like our identity, gender, and well-being are easily inferred using deep acoustic models, we encounter a new generation of privacy risks by using these services. One approach to mitigate the risk of paralinguistic-based privacy breaches is to exploit a combination of cloud-based processing with privacy-preserving, on-device paralinguistic information learning and filtering before transmitting voice data. In this article we introduce EDGY , a configurable, lightweight, disentangled representation learning framework that transforms and filters high-dimensional voice data to identify and contain sensitive attributes at the edge prior to offloading to the cloud. We evaluate EDGY’s on-device performance and explore optimization techniques, including model quantization and knowledge distillation, to enable private, accurate, and efficient representation learning on resource-constrained devices. Our results show that EDGY runs in tens of milliseconds with 0.2% relative improvement in “zero-shot” ABX score or minimal performance penalties of approximately 5.95% word error rate (WER) in learning linguistic representations from raw voice signals, using a CPU and a single-core ARM processor without specialized hardware.

Phone duration modeling for speaker age estimation in children.

Automatic inference of paralinguistic information from speech, such as age, is an important area of research with many technological applications. Speaker age estimation can help with age-appropriate curation of information content and personalized interactive experiences. However, automatic speaker age estimation in children is challenging due to the paucity of speech data representing the developmental spectrum, and the large signal variability including within a given age group. Most prior approaches in child speaker age estimation adopt methods directly drawn from research on adult speech. In this paper, we propose a novel technique that exploits temporal variability present in children's speech for estimation of children's age. We focus on phone durations as biomarker of children's age. Phone duration distributions are derived by forced-aligning children's speech with transcripts. Regression models are trained to predict speaker age among children studying in kindergarten up to grade 10. Experiments on two children's speech datasets are used to demonstrate the robustness and portability of proposed features over multiple domains of varying signal conditions. Phonemes contributing most to estimation of children speaker age are analyzed and presented. Experimental results suggest phone durations contain important development-related information of children. The proposed features are also suited for application under low data scenarios.

Gender detection in children’s speech utterances for human-robot interaction

The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset.

Real Time Speech Recognition in 1-Dimensional Using Convolution Neural Network

Abstract: Automatic voice recognition is a hot topic in artificial intelligence and machine learning, with the goal of creating robots that can communicate with humans through speech. Speech is an information-dense communication that includes both linguistic and paralinguistic information. Emotion is a prime example of paralinguistic information that is partially communicated through speech. Developing machines that can grasp non-linguistic information like emotion simplifies humanmachine communication by making it more natural and straightforward. The effectiveness of convolutional neural networks in recognizing speech has been examined in this study.. The input characteristics of the networks were wide-band spectrograms of voice samples. The networks were trained using voice signals produced by actors while acting out a given mood. Our models were trained and evaluated using English-language speech datasets. Two degrees of augmentations were applied to the training data in each database. To regularize the networks, the dropout approach was used. Our findings revealed that the genderagnostic, language-agnostic CNN models attained state-of-the-art accuracy, beat previously published results in the literature, and matched or even surpassed human performance on benchmark databases. Future research should look at the deep learning models' capacity to recognize speech emotion using real-world speech signals.

Application of an Improved LSTM Model to Emotion Recognition

The rise of artificial intelligence technology has promoted the development of human-computer interaction and other fields. In human-computer interaction, in order to enable the machine to accurately perceive and understand the user’s emotion in real time, thereby improving the service quality of the machine, user emotion recognition has been widely studied. In real life, because voice output not only is convenient, but also contains rich emotional information, human-computer interaction is mainly carried out in the form of voice. Speech carries a wealth of linguistic, paralinguistic, and nonlinguistic information that is essential for human-computer interaction. Understanding language information alone will not allow a computer to fully comprehend the speaker’s purpose. For computers to behave like humans, speech recognition systems must be able to process nonverbal information, such as the emotional state of the speaker. As a result, developing machine understanding of human emotions requires speech-based emotion recognition. This paper proposes an improved long short-term memory network (ILSTM) for emotion recognition. Because the initial LSTM only analyzes the preceding moment’s input, it will miss out on a lot of information for the full context scene. In this way, all the features in the speech segment can be extracted. In order to be able to select the feature that can express emotion the most among the many features, this paper also introduces the attention mechanism. Experiments are carried out on public datasets, and the experimental results show that the ILSTM used in this paper is very effective in classifying speech emotion data and the classification accuracy can reach more than 0.6. This fully shows that this research can be applied to actual products and has certain feasibility and reference value.

A Study of Content-Based Recognition of Speech Utterance

Abstract: Automated voice recognition is a branch of algorithms and cognitive computing that tries to produce robots which can interact with other folks through speaking. Speaking is a data imply suggesting includes both verbal and psycholinguistic information in addition to paralinguistic data. Emotion is a good example of paralinguistic information transmitted in part through speech. Verbal interaction gets simpler because of the development of technologies that can comprehend paralinguistic information such as mood. In this Cnns circuits' usefulness in language research emotion identification was investigated. Spectrograms with a wide range of frequencies of audio samples remained employed as only a source characteristic for the internet infrastructure The circuits had been educated. to identify speaking patterns provided by performers when expressing a certain mood. We used English-language speech resources to validate and develop our technologies The information used for retraining was expanded by two levels in each database. As a result of this, the dropout strategy was employed to. The genderagnostic, language-agnostic CNN models, as per our findings, achieved the highest degree of accuracy, beat Consequences of elections in the literary, and equalled or even surpassed human performance on benchmark databases. Future study should test the capability of deep learning methods in voice emotion identification using real-life speech data. Keywords: Artificial intelligence, Machine Learning, Speech recognition, Convolutional neural network (CNN)

Emotion Recognition With Multimodal Transformer Fusion Framework Based on Acoustic and Lexical Information

People usually express emotions through paralinguistic and linguistic information in speech. How to effectively integrate linguistic and paralinguistic information for emotion recognition is a challenge. Previous studies have adopted the bidirectional long short-term memory (BLSTM) network to extract acoustic and lexical representations followed by a concatenate layer, and this has become a common method. However, the interaction and influence between different modalities are difficult to promote using simple feature fusion for each sentence. In this article, we propose an implicitly aligned multimodal transformer fusion (IA-MMTF) framework based on acoustic features and text information. This model enables the two modalities to guide and complement each other when learning emotional representations. Thereafter, the weighed fusion is used to control the contributions of different modalities. Thus, we can obtain more complementary emotional representations. Experiments on the interactive emotional dyadic motion capture (IEMOCAP) database and multimodal emotionlines dataset (MELD) show that the proposed method outperforms the baseline BLSTM-based method.

Influences of talker gender on second language speech perception

This study examined how talker gender information may influence second language speech perception. The participants were ten right-handed male Japanese adults with normal hearing. The speech stimuli were two minimal pairs of English words, “heed-hod” and “head-had”, recorded from twenty male and twenty female native English speakers. The stimuli were delivered binaurally at a sensation level of 50 dB. In the gender recognition task, the subjects were asked to judge talker gender. In the vowel recognition task, they were asked to identify the vowel category. Magnetoencephalography data were recorded during the experiment. For gender recognition, there was no significant difference in identification accuracy between the two word pairs while reaction time was significantly faster for the difficult “head-had” pair. In the phoneme recognition task, there was a significant phoneme*gender interaction for the difficult “head-had” pair but not for the easy “heed-hod” pair. The MEG data for gender recognition showed right hemisphere dominance, and the phoneme recognition task showed overall bilateral activation. Time-frequency analysis further revealed distinctions between male and female voices and the difficulty differences for the two word pairs in the phoneme recognition task. The results suggest a complex role of paralinguistic gender information in L2 speech perception.

The development of variability in pausing and articulation rate in Hungarian speakers ten years apart

In recent years, attention given to speech-related disciplines has shifted to analyzing extra- and paralinguistic information occurring in speech. Moreover, there is a growing public interest in ageing, which, in turn, may encourage the conduct of this type of research. Earlier studies have analyzed older speakers, and, to date, we have little information about the changes taking place in the voices of healthy, young speakers growing older. Our research examines how certain temporal patterns in speech change in two different types of speech (reading and spontaneous speech) over a period of 10 years. The analysis included speech samples from 13 healthy, young adult male speakers recorded under the same conditions ten years apart. The study focuses on the timing differences within and between the recordings. The development of temporal and pausing patterns were analyzed depending on the type of speech, position, and the time of the recording (10-year difference). The results showed that not only the type of speech but the differences within and between the recordings affected the pausing strategies. In addition, the data indicated that (1) a slower articulation rate was observed in spontaneous speech and (2) higher variability between sample types i.e., a more varied articulation rate was observed in spontaneous speech than in reading. In the case of the articulation rate the individual differences dominated.

A quantitative analysis of the reactions of viewers with hearing impairment to the intralingual subtitling of Egyptian movies

This study investigates how the viewers with hearing impairment reacted to the Modern Standard Arabic (MSA) subtitles added to some Vernacular Arabic movies during the COVID-19 stay-at-home period. A sample group of 106 deaf participants was asked to watch the MSA subtitled version of the Egyptian vernacular movie, Boushkash, and fill in an 18-item questionnaire of five constructs, namely, (1) movie watching habits, (2) technical aspects, (3) linguistic and paralinguistic information, (4) attitude, and (5) future actions and recommendations. The analysis showed that the intralingual subtitling of vernacular Arabic comedy movies was received positively by the participants. The technical specifications of the subtitles were satisfactory and adequate. The paralinguistic information was helpful as it offers a better understanding of the movie and creates a sense of reality in the movie's scenes. This indicates that intralingual subtitling is a step in the right direction that makes audiovisual materials accessible to people with hearing impairment and enhances their feeling of social inclusion. The study concludes that more governmental care in the Arab countries should be directed towards this minority group by urging national TV channels to add intralingual translation to their various programs.

VoxCeleb1: Speaker Age-Group Classification using Probabilistic Neural Network

The human voice speech includes essentially paralinguistic information used in many applications for voice ‎recognition. Classifying speakers according to their age-group has been considered as a valuable tool in ‎various applications, as issuing different levels of permission for different age-groups. In the presented ‎research, an automatic system to classify speaker age-group without depending on the text is proposed. The ‎Fundamental Frequency (F0), Jitter, Shimmer, and Spectral Sub-Band Centroids (SSCs) are used as a ‎feature, while the Probabilistic Neural Network (PNN) is utilized as a classifier for the purpose of ‎classifying the speaker utterances into eight age-groups. Experiments are carried out on VoxCeleb1 dataset ‎to demonstrate the proposed system's performance, which is considered as the first effort of its kind. The ‎suggested system has an overall accuracy of roughly 90.25%, and the findings reveal that it is clearly ‎superior to a variety of base-classifiers in terms of overall accuracy.‎

Classification of Paralinguistic Information in Children's Speech Using a Database with Low Inter-Annotator Agreement and Strongly Unbalanced Classes

Classification of Paralinguistic Information in Children's Speech Using a Database with Low Inter-Annotator Agreement and Strongly Unbalanced Classes