Voice Processing Research Articles

Design, Implementation, and Practical Evaluation of a Voice Recognition Based IoT Home Automation System for Low-Resource Languages and Resource-Constrained Edge IoT Devices: A System for Galician and Mobile Opportunistic Scenarios

Systems with voice control are an attractive option for increasing technological integration, not only for people with little knowledge on technology or constrained Internet access, but also for people with certain disabilities. In addition, devices based on Alexa or Google Home provide an interesting alternative for interacting with Internet of Things (IoT) devices, but they usually rely on an Internet connection to a cloud server for their full operation. Furthermore, many voice-recognition systems are only available in a limited number of languages, which tend to be those with the highest number of speakers, thus excluding minority-language speakers. To address the previously mentioned issues, this article presents a solution based on Edge Computing and voice commands that carries out offline voice processing and that is able to interact with IoT-based systems. The proposed system performs local speech inference, providing a communication interface with IoT devices in a Bluetooth mesh, all in a fast way and without the need for an Internet connection. In addition, the proposed solution can be adapted easily for voice recognition of languages with few resources. Such a feature is demonstrated with the Galician language, which is spoken by less than 3 million people worldwide. In particular, different Automatic Speech Recognition (ASR) models based on three of the most popular ASR development frameworks (wav2vec2, DistilHubert, Whisper) were developed to transcribe short speech and to translate it into IoT commands that perform specific home-automation actions. Such models were fine-tuned for Galician with a corpus of approximately 20 hours and were evaluated in static and mobile opportunistic scenarios in terms of accuracy, energy consumption and latency on an embedded platform (that acts as an edge device) and on a cloud server. The obtained results show that inference is performed in less than 2 seconds on a Raspberry Pi 4 for the two smallest models and in less than 500 ms on a high-end Android smartphone when processing all data locally with CPU-only inference (i.e., without hardware acceleration or external processing). The results of the transcriptions are accurate enough to be able to use simple text distance algorithms to detect keywords in the speech and perform commands on IoT devices. In particular, a maximum success rate of 92% was achieved for detecting the indicated commands when using models optimized for being executed on embedded devices. For selected home scenarios, command actions were sent via Bluetooth with average response times of up to 113 ms.

Electrocorticography reveals the dynamics of famous voice responses in human fusiform gyrus.

Voice and face processing occur through convergent neural systems that facilitate speaker recognition. Neuroimaging studies suggest that familiar voice processing engages early visual cortex, including the bilateral fusiform gyrus (FG) on the basal temporal lobe. However, what role the FG plays in voice processing and whether it is driven by bottom-up or top-down mechanisms is unresolved. In this study we directly examined neural responses to famous voices and faces in human FG with direct cortical surface recordings (electrocorticography) in epilepsy surgery patients. We tested the hypothesis that neural populations in human FG respond to famous voices and investigated the temporal properties of voice responses in FG. Recordings were acquired from five adult participants during a person identification task using visual and auditory stimuli from famous speakers (U.S. Presidents Barack Obama, George W. Bush, and Bill Clinton). Patients were presented with images of presidents or clips of their voices and asked to identify the portrait/speaker. Our results demonstrate that a subset of face-responsive sites in and near FG also exhibit voice responses that are both lower in magnitude and delayed (300-600 ms) compared with visual responses. The dynamics of voice processing revealed by direct cortical recordings suggests a top-down feedback-mediated response to famous voices in FG that may facilitate speaker identification.NEW & NOTEWORTHY Interactions between auditory and visual cortices play an important role in person identification, but the dynamics of these interactions remain poorly understood. We performed direct brain recordings of fusiform face cortex in human epilepsy patients performing a famous voice naming task, revealing the dynamics of famous voice processing in human fusiform face cortex. The findings support a model of top-down interactions from auditory to visual cortex to facilitate famous voice recognition.

Psychopathic and autistic traits differentially influence the neural mechanisms of social cognition from communication signals

Psychopathy is associated with severe deviations in social behavior and cognition. While previous research described such cognitive and neural alterations in the processing of rather specific social information from human expressions, some open questions remain concerning central and differential neurocognitive deficits underlying psychopathic behavior. Here we investigated three rather unexplored factors to explain these deficits, first, by assessing psychopathy subtypes in social cognition, second, by investigating the discrimination of social communication sounds (speech, non-speech) from other non-social sounds, and third, by determining the neural overlap in social cognition impairments with autistic traits, given potential common deficits in the processing of communicative voice signals. The study was exploratory with a focus on how psychopathic and autistic traits differentially influence the function of social cognitive and affective brain networks in response to social voice stimuli. We used a parametric data analysis approach from a sample of 113 participants (47 male, 66 female) with ages ranging between 18 and 40 years (mean 25.59, SD 4.79). Our data revealed four important findings. First, we found a phenotypical overlap between secondary but not primary psychopathy with autistic traits. Second, primary psychopathy showed various neural deficits in neural voice processing nodes (speech, non-speech voices) and in brain systems for social cognition (mirroring, mentalizing, empathy, emotional contagion). Primary psychopathy also showed deficits in the basal ganglia (BG) system that seems specific to the social decoding of communicative voice signals. Third, neural deviations in secondary psychopathy were restricted to social mirroring and mentalizing impairments, but with additional and so far undescribed deficits at the level of auditory sensory processing, potentially concerning deficits in ventral auditory stream mechanisms (auditory object identification). Fourth, high autistic traits also revealed neural deviations in sensory cortices, but rather in the dorsal auditory processing streams (communicative context encoding). Taken together, social cognition of voice signals shows considerable deviations in psychopathy, with differential and newly described deficits in the BG system in primary psychopathy and at the neural level of sensory processing in secondary psychopathy. These deficits seem especially triggered during the social cognition from vocal communication signals.

Artificial intelligence for suicide assessment using Audiovisual Cues: a review

Death by suicide is the seventh leading death cause worldwide. The recent advancement in Artificial Intelligence (AI), specifically AI applications in image and voice processing, has created a promising opportunity to revolutionize suicide risk assessment. Subsequently, we have witnessed fast-growing literature of research that applies AI to extract audiovisual non-verbal cues for mental illness assessment. However, the majority of the recent works focus on depression, despite the evident difference between depression symptoms and suicidal behavior and non-verbal cues. This paper reviews recent works that study suicide ideation and suicide behavior detection through audiovisual feature analysis, mainly suicidal voice/speech acoustic features analysis and suicidal visual cues. Automatic suicide assessment is a promising research direction that is still in the early stages. Accordingly, there is a lack of large datasets that can be used to train machine learning and deep learning models proven to be effective in other, similar tasks.

Involving YOUNG people with Type 1 Diabetes in reSearch and the development of healTh cARe activities

Abstract Background Typ1 Diabetes (T1D) us a complicated condition that requires constant monitoring and making many decisions. Living with T1D means to check blood sugar levels, inject oneself insulin, and be careful what you eat. In the life of a young adult there are a lot of things going on and youngsters don't want to control themselves all day and night. They want to rebel and feel free. Methods Teenagers living with T1D are actively involved in the research process from data collection to data interpretation. Following the photo voice method the teenagers documented their everyday life with T1D by photographs and recordings (Summer 2021), and discussed their need and resources in group discussions. The visual and audio-data was structured on a muti-touchscreen for which a software was developed during the project. From the inductively formed categories, measures were derived together with the young people and transferred into an action plan, which was presented and discussed with decision-makers. Results During the photo voice process, the teenagers identified 26 categories (i.e. nutrition, coping strategies, autonomy, etc.) that were important for describing their life with diabetes and there categories were categorized into 4 dimension: psycho-social, time, subject and space. The project-team used a systematic action planning process to develop 9 goals for an action plan to improve their life with T1D. They presented their action plan to local political leaders and stakeholders from different setting, such as health care and youth work. The responses during the stakeholder dialog were positively and there was agreement to achieve the defined goals. Conclusions The photo-voice method was helpful in immersing the participants in the lives of young people with type 1 diabetes. Structuring and organizing the visual- and audio-data together was beneficial for the participatory process. Together, an action plan could be developed and discussed with decision makers. Key messages • The photo voive method using digital tools (like smartphones and multitouchscreen) is a route to empower young people and give them a voice. • The participating patients are experts for their bodies and the disease and should be perceived and taken seriously as such.

Singing professionalization peculiarities at secondary general education institutions of Kharkiv till the middle of XIX century

The purpose of the article is to determine the specifics of the professionalization of singing until the middle of the XIX century in Kharkiv general secondary education institutions.
 The methodology. Since the phenomenon of Kharkiv vocal art was not considered in the scientific discourse, the methodological basis of the article is the method of comparative analysis, the application of which made it possible to determine the specific features of professional singing in the educational environment of the city. Determining the characteristics of professional singing (methodology of voice processing, repertoire, etc.) was carried out within the framework of applying the principle of systematicity.
 The results. The specificity of the understanding of singing as a sphere of professional creativity within the limits of church choral practice is traced. The principles of processing of the singing voice in Kharkiv institutions of general secondary education from the middle of the XVIII century are considered. In the context of preparing a singer for collective music making, the music teacher — bandmaster used methods of working with the singing voice, which were formed within the church choir practice and were aimed at mastering vocal technique. For Ukrainian educational institutions of the last quarter of the XVIII century we can highlight common specifics of the activity of music classes, which consisted, in addition to the music teacher, of minor students and adult already trained musicians.
 It is noted that since the 1840s, the aspiration to professional mastery of solo singing has become popular among Kharkiv youth, which is due to the invitation of European vocalists to become a music teachers at Kharkiv educational institutions.
 The scientific novelty. It was determined that by the middle of the XIX century. vocal art was cultivated in general educational institutions of Kharkiv by bandmasters, whose multi-functional specifics of activity is determined by the mutual influence of high-quality performance of organizational, pedagogical and other functions within the framework of leadership of musical collectives. In the process of preparing the singer for collective music making, the bandmaster, firstly, used the methods of setting the voice as a means of reproducing the musical text, developed and elaborated in European musical practice; secondly, ensured learning the basic standards for vocals as a level of mastery of the vocal apparatus: voice setting, cantilena singing, purity of intonation, sense of metrorhythm, etc.
 The performing and teaching activities of European singers since the 1840s stimulated the professionalization of vocal training in general educational institutions of Kharkiv in the context of reorientation from collective to solo singing. Thanks to the pedagogical activity of the Frenchman G. Briss, the Italians Conti, L. Gervasi, and others, the vocal repertoire begins to be dominated by opera music, the reproduction of which requires professional training of the voice.
 The practical significance. Determining the logic of the formation and functioning of certain signs of professional voice reproduction of a musical text within certain chronological and cultural boundaries will contribute to the improvement of the process of understanding the phenomena of, on the one hand, the musical culture of Kharkiv, and on the other hand, the vocal art of Ukraine.

Analysis of the Microcontroller Resources Using Specifics for Speech Recognition

The use of neural networks for information recognition, in particular, voice, expands the functional capabilities of embedded systems on microcontrollers. But it is necessary to take into account the limitations of the microcontroller resources. The purpose of the work is to analyze the impact of voice processing parameters and neural network architecture on the degree of microcontroller resources usage. To do this, a database of samples of the keyword, samples of other words and voices, and samples of noise are created, the probability of recognizing the keyword among other words and noises is evaluated, the dependence of the amount of memory used on the microcontroller and the decision-making time on the number MFC coefficients is established, the dependence of the amount of used memory of the microcontroller and the decision-making time on the type of convolutional neural network is established also. During the experiment, the Arduino Nano 33 BLE Sense development board was used. The neural network model was built and trained on the Edge Impulse software platform. To conduct the experiment, three groups of data with the names "hello", "unknown", "noise" were created. The group "hello" contains 94 examples of the word "hello" in English, spoken by a female voice. The "unknown" group contains 167 examples of other words pronounced by both female and male voices. The "noise" group contains 166 samples of noise and random sounds. According to Edge Impulse's recommendation, 80% of the samples from each of the data groups were used to train the neural network model, and 20% of the samples were used for testing. Analysis of the results shows that with an increase in the number of MFC coefficients and, accordingly, the accuracy of keyword recognition, the amount of program memory occupied by the code increases by 480 bytes (less than 1%). For the nRF52840 microcontroller, this is not a significant increase. The amount of RAM used during the experiment did not change. Although the calculation time of the accuracy of the code word definition increased by only 14 ms (less than 5%) with the increase in the number of MFC coefficients, the calculation procedure is quite long (approximately 0.3 s) compared to the sound sample length of 1 s. This can be a certain limitation when processing a sound signal with 32-bit microcontrollers. To analyze phrases or sentences, it is necessary to use more powerful microcontrollers or microprocessors. Based on the results of experimental research, it can be stated that the computing resources of 32-bit microcontrollers are quite sufficient for recognizing voice commands with the possibility of pre-digital processing of the sound signal, in particular, the use of low-frequency cepstral coefficients. The selection of the number of coefficients does not significantly affect the amount of used FLASH and RAM memory of the nRF52840 microcontroller. The comparison results show the superiority of the 2D network in the accuracy of the keyword definition for both 12 and 13 MFC coefficients. The use of a one-dimensional convolutional neural network for voice sample recognition in the conducted experiment provides memory savings of approximately 5%. The quality of keyword recognition with the number of MFC coefficients of 12 is approximately 0.7. For 17 MFC coefficients, the recognition quality is already 0.97. The amount of RAM used in the case of the 2D network has decreased slightly. Voice sample processing time for both types of networks is practically the same. Thus, 1D convolutional neural networks have certain advantages in microcontroller applications for voice processing and recognition. The limitation of voice recognition on the microcontroller is the sufficiently long processing time of the sound sample (approximately 0.3 s) with the duration of the sample itself being 1 s, this can be explained by a sufficiently low clock frequency of 64 MHz. Increasing the clock frequency will reduce the calculation time.

Intramodal cortical plastic changes after moderate visual impairment in human amblyopia

SummaryEarly blindness results in alterations in the neural responses to auditory stimuli. Here we show that even moderately reduced vision in one eye early in life is sufficient to induce neural plastic changes in voice processing. We asked individuals with reduced visual acuity in one eye due to amblyopia to attend to vocal cues during electroencephalogram recording. We found enhanced frontal auditory responses at 125 ms–225 ms, which were correlated with reduced vision in the amblyopic eye, but not the fellow eye. Our results indicate intramodal reorganization, typically observed after congenital profound visual deprivation.

Who Can Afford to Voice? Examining the Role of Resources in the Employee Voice Process

Work, money, and the economy are the most common stressors for adults in the United States (APA, 2020a, 2020b). Financial stressors may highlight the importance and precarity of work—motivating employees to avoid risky behaviors, even those that may be necessary or beneficial. Thus, it is valuable to explore how financial stress may increase barriers for positive risk-taking in the workplace. Grounded in Conservation of Resources theory (Hobfoll, 1989), the current study investigated how financial stress and leader-member-exchange (LMX) affects employee behavior at work. We examined how financial stress impacts a critical, desired form of risk-taking at work: voice, through perceived riskiness of voice. Further, we consider the potential role of leadership protective resources (i.e., high-quality LMX), such that high LMX may buffer the negative association between perceived riskiness and voice. The study utilized a multi-source and multi-time point design to understand the effects of employees' financial stress on their voice behaviors at work. Results demonstrated that financial stress impacts employees' perceptions of voice riskiness, and that perceived riskiness of voice influences voice outcomes. This work offers both theoretical integration and expansion to the voice and financial stress domains, and offers actionable practical implications for organizational leaders and decision-makers.

Employee Appreciation Based On Customer Satisfaction Using IBM Cognitive Services

Abstract: The way we treat your employees is the way they treat your customers According to a recent article, creating an employee recognition culture can increase employee retention by up to 31%. Employee recognition is the process of showing appreciation for an employee’s achievements, actions, and contributions. In big companies (Amazon/Swiggy/Uber ) appreciation is given to the employees based on the customer feedback or satisfaction (chat process/ voice process). Analysing all the feedback of each customer towards an employee work manually is a tedious job. The main objective of this guided project is to appreciate the employee work based on analysis of customer feedback. We are designing an application where the HR/ user can upload the feedback report of all the employees. The analysis of each employee report is displayed on the Web application in the form of Bar charts

Hypersensitivity to passive voice hearing in hallucination proneness.

Voices are a complex and rich acoustic signal processed in an extensive cortical brain network. Specialized regions within this network support voice perception and production and may be differentially affected in pathological voice processing. For example, the experience of hallucinating voices has been linked to hyperactivity in temporal and extra-temporal voice areas, possibly extending into regions associated with vocalization. Predominant self-monitoring hypotheses ascribe a primary role of voice production regions to auditory verbal hallucinations (AVH). Alternative postulations view a generalized perceptual salience bias as causal to AVH. These theories are not mutually exclusive as both ascribe the emergence and phenomenology of AVH to unbalanced top-down and bottom-up signal processing. The focus of the current study was to investigate the neurocognitive mechanisms underlying predisposition brain states for emergent hallucinations, detached from the effects of inner speech. Using the temporal voice area (TVA) localizer task, we explored putative hypersalient responses to passively presented sounds in relation to hallucination proneness (HP). Furthermore, to avoid confounds commonly found in in clinical samples, we employed the Launay-Slade Hallucination Scale (LSHS) for the quantification of HP levels in healthy people across an experiential continuum spanning the general population. We report increased activation in the right posterior superior temporal gyrus (pSTG) during the perception of voice features that positively correlates with increased HP scores. In line with prior results, we propose that this right-lateralized pSTG activation might indicate early hypersensitivity to acoustic features coding speaker identity that extends beyond own voice production to perception in healthy participants prone to experience AVH.

To develop and implement Smart Reader for Visually Impaired People

Authors have proposed a new device of automatic text reader for visually blind people, which is prepared using Raspberry Pi kit. The primary objective of this paper is to provide a smart reader using raspberry pi for visually challenged people. It is new, capable and cheaper method which enables the client to hear the contents of text images instead of reading through them. This technique combines the awareness of Optical nature Recognition and Text to Speech Synthesizer in Raspberry pi. It is a technique that scans and understands the English alphabets and statistics in the image with the OCR technique and converting to voices. This device introduces the incorporation of an entire Text Read-out system. This device consists of two modules, the image processing module and a voice processing module. The device is made for the conversion of the printed document into a text file by using Raspberry Pi which uses Python programming. Investigational results have exposed the usefulness of the module.

The Effects of Pitch Manipulation on Male Ratings of Female Speakers and Their Voices.

Vocal and facial cues typically co-occur in natural settings, and multisensory processing of voice and face relies on their synchronous presentation. Psychological research has examined various facial and vocal cues to attractiveness as well as to judgements of sexual dimorphism, health, and age. However, few studies have investigated the interaction of vocal and facial cues in attractiveness judgments under naturalistic conditions using dynamic, ecologically valid stimuli. Here, we used short videos or audio tracks of females speaking full sentences and used a manipulation of voice pitch to investigate cross-modal interactions of voice pitch on facial attractiveness and related ratings. Male participants had to rate attractiveness, femininity, age, and health of synchronized audio-video recordings or voices only, with either original or modified voice pitch. We expected audio stimuli with increased voice pitch to be rated as more attractive, more feminine, healthier, and younger. If auditory judgements cross-modally influence judgements of facial attributes, we additionally expected the voice pitch manipulation to affect ratings of audiovisual stimulus material. We tested 106 male participants in a within-subject design in two sessions. Analyses revealed that voice recordings with increased voice pitch were perceived to be more feminine and younger, but not more attractive or healthier. When coupled with video recordings, increased pitch lowered perceived age of faces, but did not significantly influence perceived attractiveness, femininity, or health. Our results suggest that our manipulation of voice pitch has a measurable impact on judgements of femininity and age, but does not measurably influence vocal and facial attractiveness in naturalistic conditions.

Rapid pre-attentive processing of a famous speaker: Electrophysiological effects of Angela Merkel's voice

The recognition of human speakers by their voices is a remarkable cognitive ability. Previous research has established a voice area in the right temporal cortex involved in the integration of speaker-specific acoustic features. This integration appears to occur rapidly, especially in case of familiar voices. However, the exact time course of this process is less well understood. To this end, we here investigated the automatic change detection response of the human brain while listening to the famous voice of German chancellor Angela Merkel, embedded in the context of acoustically matched voices. A classic passive oddball paradigm contrasted short word stimuli uttered by Merkel with word stimuli uttered by two unfamiliar female speakers. Electrophysiological voice processing indices from 21 participants were quantified as mismatch negativities (MMNs) and P3a differences. Cortical sources were approximated by variable resolution electromagnetic tomography. The results showed amplitude and latency effects for both MMN and P3a: The famous (familiar) voice elicited a smaller but earlier MMN than the unfamiliar voices. The P3a, by contrast, was both larger and later for the familiar than for the unfamiliar voices. Familiar-voice MMNs originated from right-hemispheric regions in temporal cortex, overlapping with the temporal voice area, while unfamiliar-voice MMNs stemmed from left superior temporal gyrus. These results suggest that the processing of a very famous voice relies on pre-attentive right temporal processing within the first 150 ms of the acoustic signal. The findings further our understanding of the neural dynamics underlying familiar voice processing.

Plant Leaf Disease Predictor Using Deep Learning

Abstract: Artificial intelligence includes deep learning as a subset. Due to its advantages, autonomous learning and feature extraction have been hotly debated in academic and industrial circles in recent years. Image and video processing, voice processing, and natural language processing have all benefited from it. Simultaneously, it has grown into a centre for agricultural plant protection research, which includes, among other things, plant disease recognition and insect range evaluation. Deep learning can assist avoid the drawbacks of artificially selecting disease spot features, improve the objectivity of plant disease feature extraction, and accelerate research and technological change. In this review, we look at how deep learning technology has progressed in the field of agricultural leaf disease detection in recent years. The current trends and challenges in using deep learning and sophisticated imaging techniques to detect plant leaf disease are discussed in this paper. Our findings are expected to be valuable to researchers interested in detecting plant diseases and insect pests. We also discussed some of the current problems and issues that need to be addressed. Keywords: Analysis, Deep Learning, Prediction, Plant Leaf, Resnet 50

Jael:

After a brief analysis regarding the figure of Deborah, with this article we propose a characterization of the figure Jael in the two texts (prose and verse) of Judges 4 and 5, as well as the possible intertextual relations of her figure with specific images and contexts of the time that go back to the memories of these texts, since they are formed by varied multilayers. Despite being a polyphonic narrative in terms of modern narrative analysis theories, we do not fail to consider the memories embedded in the texts analyzed, and which constitute a fundamental role in the resistance of marginalized groups in the context of their writing. To this end, we use Bakhtin's theory of Heteroglossia and also his ideas about the presence of plurality of voices in a narrative, we also use research around Canaanite mythology and the analysis of contextual imagery around specific terms used in these texts. All this we propose in order to construct the image of the character Jael in the most explicit way possible, challenging new research and deepening regarding this imagery for the deconstruction of the silencing process of voices that groups suffered through the production of these texts.

E-Commerce Marketing Optimization of Agricultural Products Based on Deep Learning and Data Mining.

China Internet plus agriculture was first put forward in 2015 by the Chinese government's work report, laying the foundation for the development of Internet plus agriculture and promoting the rapid growth of e-commerce marketing of agricultural products. The combination of agricultural product marketing and e-commerce effectively reduces the intermediate links of agricultural product sales. Many e-commerce professional villages have sprung up in some rural areas across the country, and the number of rural e-commerce stores has continued to grow. At this stage, rural e-commerce has become a new way of agricultural trade, and rural e-commerce has formed a unique rural e-store. At present, the e-commerce market share of agricultural products in rural stores is very large, and its advantages are favored by the government, scientific research institutions, and agricultural products processing enterprises. However, with the gradual development of rural e-commerce, it has also encountered many difficulties. Based on this point, this study applies deep learning and data mining to optimize e-commerce marketing. First, with the growth of the online scale of agricultural product transaction data, the creation of traditional shallow model cannot meet the needs of online data processing. Therefore, this study decides to use the deep learning theory for optimization. It has excellent performance in the technical fields of big data processing and image and voice processing and has strong construction ability, which can effectively represent the characteristics of the model. Combined with the characteristics of e-commerce agricultural products processing and consumer practice, this study designs and develops a new customer value evaluation model based on data mining and e-commerce agricultural products value characteristics in the field of e-commerce. By combining deep learning and data mining technology, this study applies it to the field of e-commerce, so as to promote the transformation of marketing optimization.

Categorization of vocal and nonvocal stimuli in Guinea baboons (Papio papio).

Categorization of vocal sounds apart from other sounds is one of the key abilities in human voice processing, but whether this ability is present in other animals, particularly nonhuman primates, remains unclear. In the present study, 25 socially housed Guinea baboons (Papio papio) were tested on a vocal/nonvocal categorization task using Go/Nogo paradigm implemented on freely accessible automated learning devices. Three individuals from the group successfully learned to sort Grunt vocalizations from nonvocal sounds, and they generalized to new stimuli from the two categories, indicating that some baboons have the ability to develop open-ended categories in the auditory domain. Contrary to our hypothesis based on the human literature, these monkeys learned the nonvocal category faster than the Grunt category. Moreover, they failed to generalize their classification to new classes of conspecific vocalizations (wahoo, bark, yak, and copulation calls), and they categorized human vocalizations in the nonvocal category, suggesting that they had failed to represent the task as a vocal versus nonvocal categorization problem. Thus, our results do not confirm the existence of a separate perceptual category for conspecific vocalizations in baboons. Interestingly, the three successful baboons are the youngest of the group, with less training in visual tasks, which supports previous reports of age and learning history as crucial factors in auditory laboratory experiments.

Correlates of individual voice and face preferential responses during resting state

Human nonverbal social signals are transmitted to a large extent by vocal and facial cues. The prominent importance of these cues is reflected in specialized cerebral regions which preferentially respond to these stimuli, e.g. the temporal voice area (TVA) for human voices and the fusiform face area (FFA) for human faces. But it remained up to date unknown whether there are respective specializations during resting state, i.e. in the absence of any cues, and if so, whether these representations share neural substrates across sensory modalities. In the present study, resting state functional connectivity (RSFC) as well as voice- and face-preferential activations were analysed from functional magnetic resonance imaging (fMRI) data sets of 60 healthy individuals. Data analysis comprised seed-based analyses using the TVA and FFA as regions of interest (ROIs) as well as multi voxel pattern analyses (MVPA). Using the face- and voice-preferential responses of the FFA and TVA as regressors, we identified several correlating clusters during resting state spread across frontal, temporal, parietal and occipital regions. Using these regions as seeds, characteristic and distinct network patterns were apparent with a predominantly convergent pattern for the bilateral TVAs whereas a largely divergent pattern was observed for the bilateral FFAs. One region in the anterior medial frontal cortex displayed a maximum of supramodal convergence of informative connectivity patterns reflecting voice- and face-preferential responses of both TVAs and the right FFA, pointing to shared neural resources in supramodal voice and face processing. The association of individual voice- and face-preferential neural activity with resting state connectivity patterns may support the perspective of a network function of the brain beyond an activation of specialized regions.

Chinese Studios in the Russian Theater University. Ethnopsychological Approach in Teaching Stage Speech

The relevant topic of teaching foreign-speaking students in the field of modern Russian theater education is discussed. The subject of the research is the speech training of Chinese students-actors within the framework of an experimental Russian–Chinese theater and educational project of the Russian State Institute of Performing Arts (St. Petersburg) and the Central Academy of Drama (Beijing). The experience of voice and speech training in homogeneous Chinese groups is summarized, the real manifestations of significant ethnopsychological phenomena and the possibility of their use for educational purposes are generalized. The author, a direct participant in the project, analyzes the course and results of the experiment from the position of optimal compliance of the educational process with the identified psychological, mental and national-cultural characteristics of Chinese students-actors. The novelty of the research lies in the experimental substantiation of the variant of the ethnooriented model of voice and speech training of Chinese students in the Russian theater school, carried out using two languages (Russian and Chinese) in Russia, and then in China. This ensures the consistency and meaning of voice and speech education, the possibility of transferring acquired speech skills into national artistic practice. The result of the study ethnopsychological, mental and national-cultural features were found in practice and affecting the process of voice and speech training. Taking into account and using a combination of these factors implements an ethnopsychological approach to voice and speech training of Chinese students-actors.