Journal articles on the topic 'Synthesized speech detection'

The paper focuses on the description of a system for the automatic evaluation of synthetic speech quality based on the Gaussian mixture model (GMM) classifier. The speech material originating from a real speaker is compared with synthesized material to determine similarities or differences between them. The final evaluation order is determined by distances in the Pleasure-Arousal (P-A) space between the original and synthetic speech using different synthesis and/or prosody manipulation methods implemented in the Czech text-to-speech system. The GMM models for continual 2D detection of P-A classes are trained using the sound/speech material from the databases without any relation to the original speech or the synthesized sentences. Preliminary and auxiliary analyses show a substantial influence of the number of mixtures, the number and type of the speech features used the size of the processed speech material, as well as the type of the database used for the creation of the GMMs on the P-A classification process and on the final evaluation result. The main evaluation experiments confirm the functionality of the system developed. The objective evaluation results obtained are principally correlated with the subjective ratings of human evaluators; however, partial differences were indicated, so a subsequent detailed investigation must be performed.

Steganalysis of adaptive multirate (AMR) speech is a significant research topic for preventing cybercrimes based on steganography in mobile speech services. Differing from the state-of-the-art works, this paper focuses on steganalysis of AMR speech with unknown embedding rate, where we present three schemes based on support-vector-machine to address the concern. The first two schemes evolve from the existing image steganalysis schemes, which adopt different global classifiers. One is trained on a comprehensive speech sample set including original samples and steganographic samples with various embedding rates, while the other is trained on a particular speech sample set containing original samples and steganographic samples with uniform distributions of embedded information. Further, we present a hybrid steganalysis scheme, which employs Dempster–Shafer theory (DST) to fuse all the evidence from multiple specific classifiers and provide a synthesized detection result. All the steganalysis schemes are evaluated using the well-selected feature set based on statistical characteristics of pulse pairs and compared with the optimal steganalysis that adopts specialized classifiers for corresponding embedding rates. The experimental results demonstrate that all the three steganalysis schemes are feasible and effective for detecting the existing steganographic methods with unknown embedding rates in AMR speech streams, while the DST-based scheme outperforms the others overall.

In order to improve the utilization of transmission bandwidth in voice communication, this paper proposes a discontinuous transmission method for LPC speech codec. Firstly, by using the algorithm of voice activity detection (VAD), the received signal is divided into voice frame and mute frame. Transitional frame is introduced when the voice frame is converted to mute frame. Then voice frames and transitional frames are encoded at a normal rate, but mute frames are encoded into silence description (SID) frame at a lower rate, which is sent by a method of discontinuous transmission mode. The transmission frequency of SID frame is adjusted automatically according to the fluctuation of characteristic parameters of background noise in mute frames. Finally, the method is applied to the simulation in the MELP vocoder, and the results show that this method has better adaptability in the transmission of mute signal and the synthesized background noise presents good comfort and continuity in the auditory perception.

Methods for text detection and recognition in images of natural scenes have become an active research topic in computer vision and have obtained encouraging achievements over several benchmarks. In this paper, we introduce a robust yet simple pipeline that produces accurate and fast text detection and recognition for the Uzbek language in natural scene images using a fully convolutional network and the Tesseract OCR engine. First, the text detection step quickly predicts text in random orientations in full-color images with a single fully convolutional neural network, discarding redundant intermediate stages. Then, the text recognition step recognizes the Uzbek language, including both the Latin and Cyrillic alphabets, using a trained Tesseract OCR engine. Finally, the recognized text can be pronounced using the Uzbek language text-to-speech synthesizer. The proposed method was tested on the ICDAR 2013, ICDAR 2015 and MSRA-TD500 datasets, and it showed an advantage in efficiently detecting and recognizing text from natural scene images for assisting the visually impaired.

Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven regional Bangla dialects with the goal of detecting synthesized Bangla speech and categorizing it. To categorize the regional language spoken by the speaker in the Bangla speech and determine its authenticity, the proposed model was created; a Stacked Convolutional Autoencoder (SCAE) and a Sequence of Multi-Label Extreme Learning machines (MLELM). SCAE creates a detailed feature map by identifying the spatial and temporal salient qualities from MFEC input data. The feature map is then sent to MLELM networks to generate soft labels and then hard labels. As aging generates physiological changes in the brain that alter the processing of aural information, the model took age class into account while generating dialect class labels, increasing classification accuracy from 85% to 95% without and with age class consideration, respectively. The classification accuracy for synthesized Bangla speech labels is 95%. The proposed methodology works well with English speaking audio sets as well.

Problem Addressed. Decoding of silent vocalization would be enhanced by detecting vocalization onset. This is necessary in order to improve decoding of neural firings and thus synthesize near conversational speech in locked-in subjects implanted with brain computer interfacing devices. Methodology. Cortical recordings were obtained during attempts at inner speech in a mute and paralyzed subject (ER) implanted with a recording electrode to detect and analyze lower beta band peaks meeting the criterion of a minimum 0.2% increase in the power spectrum density (PSD). To provide supporting data, three speaking subjects were used in a similar testing paradigm using EEG signals recorded over the speech area. Results. Conspicuous lower beta band peaks were identified around the time of assumed speech onset. The correlations between single unit firings, recorded at the same time as the continuous neural signals, were found to increase after the lower beta band peaks as compared to before the peaks. Studies in the nonparalyzed control individuals suggested that the lower beta band peaks were related to the movement of the articulators of speech (tongue, jaw, and lips), not to higher order speech processes. Significance and Potential Impact. The results indicate that the onset of silent and overt speech is associated with a sharp peak in lower beta band activity—an important step in the development of a speech prosthesis. This raises the possibility of using these peaks in online applications to assist decoding paradigms being developed to decode speech from neural signal recordings in mute humans.

In many complex data-rich domains, safety is highly dependent on the timely and reliable detection and identification of alarms. However, due to the coupling and complexity of systems in these environments, large numbers of alarms can occur within a short period of time – a problem called an alarm flood (Perrow, 2011). Alarm floods have been defined as more than 10 alarms in a 10-minute period (EEMUA, 1999); however, this rate is often exceeded which can lead to operators missing or misinterpreting critical alarms and, as a result, system failures and accidents. Various types of masking effects may account for observed failures to detect and identify alarms during an alarm flood. Masking occurs when one stimulus is obscured by the presence of another stimulus that appears either simultaneously or in close temporal proximity (Enns & Di Lollo V, 2000). One example of masking is an attentional blink, where the second of two stimuli is missed when presented in close temporal proximity to a preceding stimulus (Raymond, Shapiro, & Arnell, 1992). To date, attentional blinks have been studied almost exclusively in the context of two target stimuli of very short duration (less than 100ms) and in simple single-task conditions. These experiments suggest that the phenomenon occurs when two stimuli are separated by 200-600ms. However, there is limited empirical evidence (e.g., Ferris et al., 2006) that, in more complex and demanding task environments, detection performance suffers even with a longer stimulus onset asynchrony (SOA). To better predict and prevent the occurrence of attentional blinks in alarm floods, the current study aimed to establish the SOA range that results in missed signals in the context of multiple visual and auditory alarms in a multi-task environment. The participants in this study were 26 students from the University of Michigan (age: 20-30 years old). The experiment was conducted using a simulation of an automated package delivery system. Participants were required to monitor the performance of eight delivery drones and perform two tasks: (1) search and confirm that a delivery pad was present before agreeing to package delivery; (2) detect and respond to visual alarms and auditory alarms associated with the various drones. Visual alarms took the form of a number presented in the center of the screen that identified the affected drone; auditory alarms used synthesized speech to present the drone number. Participants had to acknowledge the alarm as quickly as possible by pressing a button adjacent to the drone window. Both visual and auditory alarms lasted 200ms. Crossmodal matching was performed to ensure that the perceived intensity of signals in the two modalities was the same for each individual (see Pitts, Riggs, & Sarter, 2016). Alarms appeared either by themselves (single alarms) or in close temporal proximity of another alarm (alarm pairs). Each experiment scenario was 30 minutes long and included 40 single alarms and 40 alarm pairs. In addition, a 3-minute alarm flood was included in each scenario, consisting of 30 single alarms and 30 alarm pairs. The experiment employed a 5×4 full factorial design. The two independent variables, both varied within subjects, were SOA (200, 600, 800, 1000, 1200ms) and modality pairs (all four combinations of visual and auditory alarms). The dependent measures in this study were detection rate, accuracy of identification, and response time. The detection rate for visual alarms was lower when the alarm was the second in an alarm pair, compared to single visual alarms (89.9% vs. 93.9%; X2 (2, N = 22) = 6.874, p < .01). This effect was independent of the modality of the first alarm and strongest with an SOA of 1000ms. No difference was observed for the detection of single versus paired auditory alarms. Identification accuracy for visual alarms was also significantly lower when the alarm appeared second in a pair, compared to single visual alarms (86.0% vs. 94.0%; X2 (2, N = 22) = 6.007, p = .05). This effect was also independent of the modality of the first alarm, but found only with SOAs of 600, 1000, or 1200ms. Also, no significant difference in accuracy was found for single versus paired auditory alarms. Finally, response times were significantly faster during alarm floods, compared to single alarms or alarm pairs (2160ms vs. 2318ms; F (1, 21) = 6.284, p = .001). Response times to visual and auditory alarms did not differ significantly during alarm floods. In summary, in this experiment, alarm detection and identification suffered when a visual (but not an auditory) alarm was preceded by another visual or auditory alarm. This performance decrement was observed at longer SOAs than reported in earlier single-task studies. This finding may be explained, in part, by the competing visual (but not auditory) demands imposed by the required response to the alarms. Performance during alarm floods was comparable, and even improved in terms of response times, compared to single alarms and alarm pairs. This finding may be explained by the Yerkes-Dodson Law (1908) which describes that performance improves with physiological or mental arousal, up to a point, and then decreases again when arousal increases further. Another possible explanation is that participants invested more effort during alarm floods. The findings from this study add to the knowledge base in attention and alarm design. They highlight the importance of examining attentional phenomena in applied settings to be able to predict and counter performance breakdowns that may be experienced by operators engaged in multitasking in complex data-rich environments.

The proposed system aims to lessen the number of accidents that occur due to drivers’ drowsiness and fatigue, which will in turn increase transportation safety. This is becoming a common reason for accidents in recent times. Several faces and body gestures are considered such as signs of drowsiness and fatigue in drivers, including tiredness in eyes and yawning. These features are an indication that the driver’s condition is improper. EAR (Eye Aspect Ratio) computes the ratio of distances between the horizontal and vertical eye landmarks which is required for detection of drowsiness. For the purpose of yawn detection, a YAWN value is calculated using the distance between the lower lip and the upper lip, and the distance will be compared against a threshold value. We have deployed an eSpeak module (text to speech synthesizer) which is used for giving appropriate voice alerts when the driver is feeling drowsy or is yawning. The proposed system is designed to decrease the rate of accidents and to contribute to the technology with the goal to prevent fatalities caused due to road accidents.

Eye gazing is the fundamental nonverbal interaction that is presently strengthening in emerging technology. This eye blink device facilitates communication among people with disabilities. The process is so simple that it can be done with the eyes blinking on the specific keys built into the virtual keyboard. This type of system may synthesize speech, regulate his environment, and provide a significant boost in self-belief in the individual. Our study emphasises the virtual keyboard, which not only includes integrated alphabetic keys but also contains emergency phrases that may seek help in a variety of scenarios. It can, however, provide voice notification and speech assistance to those who are speech-impaired. To get this, we employed our PC/computer digital Digi-Cam, which is integrated and recognises the face and its elements. As a result, the technique for detecting the face is far less complicated than everything else. The blink of an eye provides an opportunity for a mouse to click on the digital interface. Our goal is to provide nonverbal communication, and as a result, physically impaired people should be able to communicate with the use of a voice assistant. This type of innovation is a blessing for those who have lost their voice or are suffering from paralytic ailments.

Music and speech detection provides us valuable information regarding the nature of content in broadcast audio. It helps detect acoustic regions that contain speech, voice over music, only music, or silence. In recent years, there have been developments in machine learning algorithms to accomplish this task. However, broadcast audio is generally well-mixed and copyrighted, which makes it challenging to share across research groups. In this study, we address the challenges encountered in automatically synthesising data that resembles a radio broadcast. Firstly, we compare state-of-the-art neural network architectures such as CNN, GRU, LSTM, TCN, and CRNN. Later, we investigate how audio ducking of background music impacts the precision and recall of the machine learning algorithm. Thirdly, we examine how the quantity of synthetic training data impacts the results. Finally, we evaluate the effectiveness of synthesised, real-world, and combined approaches for training models, to understand if the synthetic data presents any additional value. Amongst the network architectures, CRNN was the best performing network. Results also show that the minimum level of audio ducking preferred by the machine learning algorithm was similar to that of human listeners. After testing our model on in-house and public datasets, we observe that our proposed synthesis technique outperforms real-world data in some cases and serves as a promising alternative.

Abstract Negative affect and neurobehavioral symptoms occur in most people with dementia and significantly impact their health outcomes and sense of wellbeing. Detecting these symptoms in this population is challenging due to associated cognitive impairment and communication difficulties. Innovative technology and artificial intelligence (AI)-assisted tools are emerging for assessing affect and neurobehavioral symptoms in individuals with dementia. This review synthesizes research evidence to identify existing AI-assisted measurement tools and evaluate their accuracy in assessing affect and symptoms in people with mild cognitive impairment and dementia. PubMed, CINAHL, Scopus, and Web of Science databases were searched. Eight articles were identified. Multiple machine learning (ML) models were developed to assess affect, apathy, anxiety, depression, agitation, and wandering. One ML model detected positive and negative affect via facial expression with an overall accuracy of 86%. One ML model detected apathy based on speech and achieved an area under curve (AUC) accuracy of 0.77–0.88. Another speech-based ML model, based on paralinguistic markers, predicted apathy, anxiety, and depression by ≥0.3 points. Another model detected wandering based on activity monitoring data and showed 98% sensitivity and specificity. Furthermore, multiple ML models were developed to detect agitation using multi-modal sensors with AUC ranging from 0.50–0.82. Findings suggest that AI-assisted tools are a promising approach to detecting affect and neurobehavioral symptoms, yet the evidence is limited. More research is needed to develop comprehensive, accurate models to detect neurobehavior symptoms. The results have significant implications for supporting research and clinical practice to promote quality of care for people with dementia.

Background Hearing loss is one of the most common disabilities worldwide and affects both individual and public health. Pure tone audiometry (PTA) is the gold standard for hearing assessment, but it is often not available in many settings, given its high cost and demand for human resources. Smartphone-based audiometry may be equally effective and can improve access to adequate hearing evaluations. Objective The aim of this systematic review is to synthesize the current evidence of the role of smartphone-based audiometry in hearing assessments and further explore the factors that influence its diagnostic accuracy. Methods Five databases—PubMed, Embase, Cochrane Library, Web of Science, and Scopus—were queried to identify original studies that examined the diagnostic accuracy of hearing loss measurement using smartphone-based devices with conventional PTA as a reference test. A bivariate random-effects meta-analysis was performed to estimate the pooled sensitivity and specificity. The factors associated with diagnostic accuracy were identified using a bivariate meta-regression model. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Results In all, 25 studies with a total of 4470 patients were included in the meta-analysis. The overall sensitivity, specificity, and area under the receiver operating characteristic curve for smartphone-based audiometry were 89% (95% CI 83%-93%), 93% (95% CI 87%-97%), and 0.96 (95% CI 0.93-0.97), respectively; the corresponding values for the smartphone-based speech recognition test were 91% (95% CI 86%-94%), 88% (95% CI 75%-94%), and 0.93 (95% CI 0.90-0.95), respectively. Meta-regression analysis revealed that patient age, equipment used, and the presence of soundproof booths were significantly related to diagnostic accuracy. Conclusions We have presented comprehensive evidence regarding the effectiveness of smartphone-based tests in diagnosing hearing loss. Smartphone-based audiometry may serve as an accurate and accessible approach to hearing evaluations, especially in settings where conventional PTA is unavailable.

Involuntarily celibate men (Incels) form online communities in which they “often bemoan their lack of a loving relationship with a woman while simultaneously dehumanizing women and calling for misogynistic violence” (Glace et al., 2021, p. 288). Several studies investigate this dehumanization and misogyny including (gendered) hate speech in online comments from Incels (e.g., Glace et al., 2021). However, not all online comments from Incels contain misogyny or gendered hate speech. To get a better understanding of the phenomenon of Incels, it would be better to not only focus on these problematic comments. Thus, we propose a new construct called “Online Incel speech”, which is defined as the sum of all online comments from Incels that are related to Inceldom, that is, being or becoming an Incel. In an approach to provide an extensive system of categorization, Grau Chopite (2022) synthesized codebooks from several studies on Incels (see example studies table note) and put it to an empirical test. She found that most Incel comments found online can be categorized into three subdimensions. The first two subdimensions cover framing by Incels, namely how Incels frame the subjective causes of becoming an Incel and how they frame the subjective emotional consequences of being an Incel. Both subdimensions can also be interpreted as part of a subjective theory (sensu Groeben et al., 1988) of Inceldom. In contrast to this, the third subdimension does not consist of framing, but of observable verbal behaviors, which are often linked to gendered hate speech. When trying to categorize online comments from Incels, former studies often applied the construct “Hybrid Masculinities” (e.g., Glace et al, 2021). This construct from Bridge and Pascoe (2014) suggests that “some men develop masculinities which appear to subvert, but actually reaffirm, White hegemonic masculinities” (Glace et al., 2021, p. 289). Glace et al. (2021) structure the construct into three subdimensions, namely (1) discursive distancing (claiming distance from hegemonic masculine roles without actually relinquishing masculine power), (2) strategic borrowing (appropriating the cultures of nondominant groups of men), and (3) fortifying boundaries (continually using hegemonic standards to constrain masculinity and demeaning men who fail to meet them). However, the construct only covers a part of Inceldom, which Glace et al. (2021) indirectly acknowledge by adding two inductive categories, that is, hostile sexism (shaming and degrading women) and suicidality (reporting suicidal thoughts, feelings, and intentions). Field of application/theoretical foundation: The construct “Online Incel speech” was coined by Grau Chopite (2022), and there are currently no other studies making use of it. However, there are studies (e.g., Vu & Lynn, 2020; also see the entry “Frames (Automated Content Analysis”) based on the framing theory by Entman (1991) where the subdimension “subjective causes” would correspond to Entman’s “causal interpretation frame”, while the “subjective emotional consequences” would correspond to Entman’s “problem definition frame”. The “subjective causes” also correspond to the “discursive distancing” and the “emotional consequences” to “suicidality” in the construct of Hybrid Masculinities. The third subdimension “verbal behavior” corresponds to gendered online hate speech (e.g., Döring & Mohseni, 2019), but also to “hostile sexism” and “fortifying boundaries” in the construct of Hybrid Masculinities. References/combination with other methods: The study by Grau Chopite (2022) employs a quantitative manual content analysis using a deductive approach. Studies based on the construct of Hybrid Masculinities also employ manual online content analyses or manual thematic analyses, but those are often qualitative in nature (e.g., Glace et al., 2021). Framing is also often assessed with manual content analyses (e.g., Nitsch & Lichtenstein, 2019), but newer studies try to assess it computationally (e.g., Vu & Lynn, 2020). Hate speech is often assessed with manual content analyses (e.g., Döring & Mohseni, 2019) and surveys (e.g., Oksanen et al., 2014), but some newer studies try to assess it computationally (e.g., Al-Hassan & Al-Dossari, 2019). As Online Incel Speech is related to framing and gendered hate speech, it seems plausible that manual content analyses of Online Incel Speech could be combined with computational analyses, too, to enable the investigation of large samples. However, computational analyses of subtle forms of verbal behavior can be challenging because the number of wrong categorizations increases (e.g., for sexism detection see Samory et al., 2021; for hate speech detection see Ruiter et al., 2022). Example studies: Example study Construct Dimensions Explanation Reliability Online Incel speech Grau Chopite (2022) Subjective Causes of Inceldom Race/Ethnicity having certain racial features and/or belonging to a certain ethnic κ = .55;AC1 = .80 Mental Health suffering from any mental health issue κ = .58;AC1 = .90 Employment difficulties with getting and/or maintaining employment; experiencing dissatisfaction in the workplace κ = .85;AC1 = .98 Family having family issues (e.g., an abusive family member) κ = .66;AC1 = .98 Subjective Emotional Consequences of Inceldom Hopelessness expressing hopelessness κ = .37;AC1 = .89 Sadness expressing sadness κ = .26;AC1 = .91 Suicidality expressing suicidality κ = .24;AC1 = .95 Anger expressing anger κ = .44;AC1 = .87 Hatred expressing hatred κ = .40;AC1 = .83 Verbal Behavior of Incels Using Gendered Hate Speech Against Women hostile sexism against women and misogynistic speech κ = .80;AC1 = .87 Adopting Social Justice Language claiming unfairness/ injustice of being discriminated by society or groups (e.g., other men, other races) κ = .48;AC1 = .82 Claiming Lack of Masculine Traits lacking masculine traits (e.g., muscles, a big penis) κ = .62;AC1 = .86 Shaming Other Men shaming of other men directly by calling them terms related to being “effeminate” or “unmanly” κ = .71;AC1 = .91 Claiming Lack of Female Interest being unable to attract women or being rejected by women κ = .61;AC1 = .87 Hybrid Masculinities Glace et al. (2021) Discursive Distancing Lack of Female Interest claiming a lack of ability to attract female romantic companionship and sexual interest n/a Lack of Masculine Traits claiming a lack of traditionally attractive masculine physical traits n/a Strategic Borrowing Race and Racism appropriating the culture of racial and ethnic minority men n/a Social Justice Language using the language of the marginalized to diminish one’s own position of power n/a Fortifying Boundaries Soyboys deriding non-Incel men as weak and desperate n/a Cucks deriding non-Incel men as being cheated or exploited by women n/a Hostile Sexism Women are Ugly deriding women for being unattractive n/a Slut-Shaming deriding women for having sex n/a False Rape Claims claiming that women make false rape claims (e.g., when approached by an Incel) n/a Women’s Only Value is Sex claiming that women’s only value is their sexuality n/a Women are Subhuman dehumanizing women n/a Suicidality Due to Incel Experience attributing suicidal thoughts, feelings, and intentions to Incel status n/a The “Clown World” claiming that the world is meaningless and nonsensical n/a Note: The codebook from Grau Chopite (2022) is based on the codebook and findings of Glace et al. (2021) and other studies (Baele et al., 2019; Bou-Franch & Garcés-Conejos Blitvich, 2021; Bridges & Pascoe, 2014; Cottee, 2020; Döring & Mohseni, 2019; D’Souza et al., 2018; Marwick & Caplan, 2018; Mattheis & Waltman, 2021; Maxwell et al., 2020; Rogers et al., 2015; Rouda & Siegel, 2020; Scaptura & Boyle, 2019; Williams & Arntfield, 2020; Williams et al., 2021). Gwet’s AC1 was calculated in addition to Cohen’s Kappa because some categories were rarely coded, which biases Cohen’s Kappa. The codebook is available at http://doi.org/10.23668/psycharchives.5626 References Al-Hassan, A., & Al-Dossari, Hmood (2019). Detection of hate speech in social networks: A survey on multilingual corpus. In D. Nagamalai & D. C. Wyld (Eds.), Computer Science & Information Technology. Proceedings of the 6th International Conference on Computer Science and Information Technology (pp. 83–100). AIRCC Publishing. doi:10.5121/csit.2019.90208 Baele, S. J., Brace, L., & Coan, T. G. (2019). From “Incel” to “Saint”: Analyzing the violent worldview behind the 2018 Toronto attack. Terrorism and Political Violence, 1–25. doi:10.1080/09546553.2019.1638256 Bou-Franch, P., & Garcés-Conejos Blitvich, P. (2021). Gender ideology and social identity processes in online language aggression against women. In R. M. DeKeyser (Ed.), Benjamins Current Topics: Vol. 116. Aptitude-Treatment Interaction in Second Language Learning (Vol. 86, pp. 59–81). John Benjamins Publishing Company. doi:10.1075/bct.86.03bou Bridges, T., & Pascoe, C. J. (2014). Hybrid masculinities: New directions in the sociology of men and masculinities. Sociology Compass, 8(3), 246–258. doi:10.1111/soc4.12134 Cottee, S. (2021). Incel (e)motives: Resentment, shame and revenge. Studies in Conflict & Terrorism, 44(2), 93–114. doi:10.1080/1057610X.2020.1822589 Döring, N., & Mohseni, M. R. (2018). Male dominance and sexism on YouTube: Results of three content analyses. Feminist Media Studies, 19(4), 512–524. doi:10.1080/14680777.2018.1467945 D'Souza, T., Griffin, L., Shackelton, N., & Walt, D. (2018). Harming women with words: The failure of Australian law to prohibit gendered hate speech. University of New South Wales Law Journal, 41(3), 939–976. Entman, R. M. 1991. Framing U.S. coverage of international news: contrasts in narratives of the KAL and Iran Air incidents. Journal of Communication, 41(4), 6-7. Glace, A. M., Dover, T. L., & Zatkin, J. G. (2021). Taking the black pill: An empirical analysis of the “Incel”. Psychology of Men & Masculinities, 22(2), 288–297. doi:10.1037/men0000328 Grau Chopite, J. (2022). Framing of Inceldom on incels.is: A content analysis [Master’s thesis, TU Ilmenau]. Psycharchives. doi:10.23668/psycharchives.5626 Groeben, N., Wahl, D., Schlee, J., & Scheele, B. (Eds.). (1988). Das Forschungsprogramm Subjektive Theorien: eine Einführung in die Psychologie des reflexiven Subjekts. Francke. Retrieved from https://nbn-resolving.org/urn:nbn:de:0168-ssoar-27658 Marwick, A. E., & Caplan, R. (2018). Drinking male tears: language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543–559. doi:10.1080/14680777.2018.1450568 Mattheis, A. A., & Waltman, M. S. (2021). Gendered hate online. In K. Ross & I. Bachmann (Eds.), The Wiley Blackwell-ICA international encyclopedias of communication. The international encyclopedia of gender, media, and communication (pp. 1–5). John Wiley & Sons Inc. doi:10.1002/9781119429128.iegmc019 Maxwell, D., Robinson, S. R., Williams, J. R., & Keaton, C. (2020). “A short story of a lonely guy”: A qualitative thematic analysis of involuntary celibacy using Reddit. Sexuality & Culture, 24(6), 1852–1874. doi:10.1007/s12119-020-09724-6 Nitsch, C. & Lichtenstein, D. (2019). Satirizing international crises. The depiction of the Ukraine, Greek debt and migration crises in political satire. Studies in Communication Science (SComS), 19(1), 85-103. doi:10.24434/j.scoms.2019.01.007 Oksanen, A., Hawdon, J., Holkeri, E., Näsi, M., & Räsänen, P. (2014). Exposure to online hate among young social media users. In N. Warehime (Ed.), Soul of Society: A focus on the lives of children & youth (p. 253-273). doi:10.1108/S1537-466120140000018021 Rogers, D. L., Cervantes, E., & Espinosa, J. C. (2015). Development and validation of the belief in female sexual deceptiveness scale. Journal of Interpersonal Violence, 30(5), 744–761. doi:10.1177/0886260514536282 Rouda, B., & Siegel, A. (2020). I’d kill for a girl like that”: The black pill and the Incel uprising. International Multidisciplinary Program in the Humanities, Tel Aviv University. Retrieved from https://www.academia.edu/43663741/_Id_kill_for_a_girl_like_that_The_Black_Pill_and_the_Incel_Uprising Ruiter, D., Reiners, L., Geet D’Sa, A., Kleinbauer, Th., Fohr, D., Illina, I., Klakow. D., Schemer, Ch., & Monnier, A. (2022). Placing m-phasis on the plurality of hate. A feature-based corpus of hate online. Preprint. Retrieved from https://doi.org/10.48550/arXiv.2204.13400 Samory, M., Sen, I., Kohne, J., Flöck, F., & Wagner, C. (2021). “Call me sexist, but...”: Revisiting sexism detection using psychological scales and adversarial samples. Proceedings of the International AAAI Conference on Web and Social Media, 15(1), 573-584. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/18085 Scaptura, M. N., & Boyle, K. M. (2019). Masculinity threat, “Incel” traits, and violent fantasies among heterosexual men in the United States. Feminist Criminology, 15(3), 278–298. doi:10.1177/1557085119896415 Vu, H. T., & Lynn, N. (2020). When the news takes sides: Automated framing analysis of news coverage of the Rohingya crisis by the elite press from three countries. Journalism Studies. Online first publication. doi:10.1080/1461670X.2020.1745665 Williams, D. J., & Arntfield, M. (2020). Extreme sex-negativity: An examination of helplessness, hopelessness, and misattribution of blame among “Incel” multiple homicide offenders. Journal of Positive Sexuality, 6(1), 33–42. doi:10.51681/1.613 Williams, D. J., Arntfield, M., Schaal, K., & Vincent, J. (2021). Wanting sex and willing to kill: Examining demographic and cognitive characteristics of violent "involuntary celibates". Behavioral Sciences & the Law, 39(4), 386–401. doi:10.1002/bsl.2512

Applications of science and technology have made a human life much easier. Vision plays a very important role in one’s life. Disease, accidents or due some other reasons people may loose their vision. Navigation becomes a major problem for the people with complete blindness or partial blindness. This paper aims to provide navigation guidance for visually impaired. Here we have designed a model which provides the instruction for the visionless people to navigate freely. NoIR camera is used to capture the picture around the person and identifies the objects. Using earphones voice output is provided defining the objects. This model includes Raspberry Pi 3 processor which collects the objects in surroundings and converts them into voice message, NoIR camera is used detect the object, power bank provides the power and earphones are used here the output message. TensorFlow API an open source software library used for object detection and classification. Using TensorFlow API multiple objects are obtained in a single frame. eSpeak a Text to Speech synthesizer (TTS) software is used to convert text (detected objects) to speech format. Hence using NoIR camera video which is captured is converted into voice output which provides the guidance for detecting objects. Using COCO model 90 commonly used objects are identified like person, table, book etc.

There is increasin demand for smart widgets which make people more comfortable. Though many research works have done on current existing devices/systems for visually impaired people are not providing facilities them enough. The imperceptible people read Braille scripted books only, so here developing a new device that will assist the visually impaired people and also providing desired language reading facility. This smart assistive device will help visually impaired people gain increased independence and freedom in society. This device has an obstacle detection sensor to intimate the obstacles to the visually impaired person and a camera connected to Raspberry pi to convert image to text using Optical Character Recognition (OCR). The read data is converted to speech using text to speech synthesizer. This will useful for visually impaired people for surviving in outdoor environment as well as reading books which are in normal script. The read data can be stored in database for further reading and it can be retrieve by giving a command.

Purpose The purpose of this study is to describe and synthesize existing research on nonstandardized assessment of cognitive-communication abilities in children with traumatic brain injury (TBI) in order to improve the detection, diagnosis, and tracking of injury sequelae and guide appropriate service provision. Materials and Method A search of peer-reviewed journal databases revealed 504 unique articles published between January 2000 and August 2019. For full inclusion, articles had to report on empirical studies examining variables related to the nonstandardized assessment of cognitive-communication skills following TBI in children. Review articles, expert opinion pieces, and non–English language articles were excluded. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews guided this process. Results Results were tabulated for each of the 14 articles that met full inclusion criteria. Included studies presented five different types of nonstandardized assessment: discourse analysis ( n = 3), systematic observation of child's performance during an instrumental activity of daily living ( n = 4), virtual reality tasks ( n = 3), structured cognitive tasks ( n = 2), and functional rating scales ( n = 2). The majority of included studies compared the outcomes of nonstandardized assessment against subtest scores and checklists drawn from a variety of existing standardized and criterion-referenced assessments. Targeted cognitive-communication skills included attention, working memory, self-regulation, planning, multitasking, social problem-solving, inferencing, and macrolevel discourse. Conclusions Preliminary research suggests that a well-designed and systematically implemented nonstandardized assessment can yield essential information about children's cognitive-communication abilities in real-world contexts. Further research is needed to validate these assessments and to determine in which settings and situations they may prove most effective. Supplemental Material https://doi.org/10.23641/asha.15079026