Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Subclinical Mild Traumatic Brain Injury (mTBI) Through Speech and Gait

Between 15% to 40% of mild traumatic brain injury (mTBI) patients experience incomplete recoveries or provide subjective reports of decreased motor abilities, despite a clinicallydetermined complete recovery. This demonstrates a need for objective measures capable of detecting subclinical residual mTBI, particularly in return-to-duty decisions for warfighters and return-to-play decisions for athletes. In this paper, we utilize features from recordings of directed speech and gait tasks completed by ten healthy controls and eleven subjects with lingering subclinical impairments from an mTBI. We hypothesize that decreased coordination and precision during fine motor movements governing speech production (articulation, phonation, and respiration), as well as during gross motor movements governing gait, can be effective indicators of subclinical mTBI. Decreases in coordination are measured from correlations of vocal acoustic feature time series and torso acceleration time series. We apply eigenspectra derived from these correlations to machine learning models to discriminate between the two subject groups. The fusion of correlation features derived from acoustic and gait time series achieve an AUC of 0.98. This highlights the potential of using the combination of vocal acoustic features from speech tasks and torso acceleration during a simple gait task as a rapid screening tool for subclinical mTBI.
Improved Speech Enhancement Using TCN with Multiple Encoder-Decoder Layers

A deep learning based time domain single-channel speech enhancement technique using multilayer encoder-decoder and a temporal convolutional network is proposed for use in applications such as smart speakers and voice assistants. The technique uses encoder-decoder with convolutional layers for obtaining representation suitable for speech enhancement and a temporal convolutional network (TCN) based separator between the encoder and decoder to learn long-range dependencies. The technique derives inspiration from speech separation techniques that use TCN based separator between a single layer encoder-decoder. We propose to use a multilayer encoder-decoder to obtain a noise-independent representation useful for separating clean speech and noise. We present t-SNE – based analysis of the representation learned using different architectures for selecting the optimal number of encoder-decoder layers. We evaluate the proposed architectures using an objective measure of speech quality, scale-invariant source-to-noise ratio, and by obtaining word error rate on a speech recognition platform. The proposed two-layer encoder-decoder architecture resulted in 48% improvement in WER over unprocessed noisy data and 33% and 44% improvement in WER over two baselines.
A Multi-Scale Fusion Framework for Bimodal Speech Emotion Recognition

Speech emotion recognition (SER) is a challenging task that requires to learn suitable features for achieving good performance. The development of deep learning techniques makes it possible to automatically extract features rather than construct hand-crafted features. In this paper, a multi-scale fusion framework named STSER is proposed for bimodal SER by us-ing speech and text information. A smodel, which takes advantage of convolutional neural network (CNN), bi-directional long short-term memory (Bi-LSTM) and the attention mechanism, is proposed to learn speech representation from the log-mel spectrogram extracted from speech data. Speciﬁcally, the CNN layers are utilized to learn local correlations. Then the Bi-LSTM layer is applied to learn long-term dependencies and contextual information. Finally, the multi-head self-attention layer makes the model focus on the features that are most re-lated to the emotions. A tmodel using a pre-trained ALBERT model is applied for learning text representation from text data. Finally, a multi-scale fusion strategy, including feature fusion and ensemble learning, is applied to improve the overall performance. Experiments conducted on the public emotion dataset IEMOCAP have shown that the proposed STSER can achieve comparable recognition accuracy with fewer feature inputs.
Deep Convolutional Spiking Neural Networks for Keyword Spotting

This paper investigates the use of deep convolutional spiking neural networks (SNN) for keyword spotting (KWS) and wakeword detection tasks. The brain-inspired SNN mimic the spike-based information processing of biological neural networks and they can operate on the emerging ultra-low power neuromorphic chips. Unlike conventional artiﬁcial neural networks (ANN), SNN process input information asynchronously in an event-driven manner. With temporally sparse input information, this event-driven processing substantially reduces the computational requirements compared to the synchronous computation performed in ANN-based KWS approaches. To explore the effectiveness and computational complexity of SNN on KWS and wakeword detection, we compare the performance and computational costs of spiking fully-connected and convolutional neural networks with ANN counterparts under clean and noisy testing conditions. The results obtained on the Speech Commands and Hey Snips corpora have shown the effectiveness of the convolutional SNN model compared to a conventional CNN with comparable performance on KWS and better performance on the wakeword detection task. With its competitive performance and reduced computational complexity, convolutional SNN models running on energy-efﬁcient neuromorphic hardware offer a low-power and effective solution for mobile KWS applications.
Semi-Supervised End-to-End ASR via Teacher-Student Learning with Conditional Posterior Distribution

Encoder-decoder based methods have become popular for automatic speech recognition (ASR), thanks to their simpliﬁed processing stages and low reliance on prior knowledge. However, large amounts of acoustic data with paired transcriptions is generally required to train an effective encoder-decoder model, which is expensive, time-consuming to be collected and not always readily available. However unpaired speech data is abundant, hence several semi-supervised learning methods, such as teacher-student (T/S) learning and pseudo-labeling, have recently been proposed to utilize this potentially valuable resource. In this paper, a novel T/S learning with conditional posterior distribution for encoder-decoder based ASR is proposed. Speciﬁcally, the 1-best hypotheses and the conditional posterior distribution from the teacher are exploited to provide more effective supervision. Combined with model perturbation techniques, the proposed method reduces WER by 19.2% relatively on the LibriSpeech benchmark, compared with a system trained using only paired data. This outperforms previous re-ported 1-best hypothesis results on the same task.
Automated Screening for Alzheimer's Dementia Through Spontaneous Speech

Dementia is a neurodegenerative disease that leads to cognitive and (eventually) physical impairments. Individuals who are affected by dementia experience deterioration in their capacity to perform day-to-day tasks thereby signiﬁcantly affecting their quality of life. This paper addresses the Interspeech 2020 Alzheimer’s’ Dementia Recognition through Spontaneous Speech (ADReSS) challenge where the objective is to propose methods for two tasks. The ﬁrst task is to identify speech recordings from individuals with dementia amongst a set of recordings which also include those from healthy individuals. The second task requires participants to estimate the Mini-Mental State Examination (MMSE) score based on an individual’s speech alone. To this end, we investigated characteristics of speech paralinguistics such as prosody, voice quality, and spectra as well as VGGish based deep acoustic embedding for automated screening for dementia based on the audio modality. In addition to this, we also computed deep text embeddings for transcripts of speech. For the classiﬁcation task, our method achieves an accuracy of 85 . 42% compared to the baseline of 62 . 50% on the test partition, meanwhile, for the regression task, our method achieves an RMSE = 4 . 30 compared to the baseline of 6 . 14 . These results show the promise of our proposed meth-ods for the task of automated screening for dementia based on speech alone.
Combining Audio and Brain Activity for Predicting Speech Quality

Since the perceived audio quality of the synthesized speech may determine a system’s market success, quality evaluations are critical. Audio quality evaluations are usually done in either subjectively or objectively. Due to their costly and time-consuming nature, the subjective approaches have generally been replaced by the faster, more cost-efﬁcient objective approaches. The primary downside of the objective approaches primarily is that they lack the human inﬂuence factors which are crucial for deriving the subjective perception of quality. However, it cannot be observed directly and manifested in individual brain activity. Thus, we combined predictions from single-subject electroencephalograph (EEG) information and audio features to improve the predictions of the overall quality of synthesized speech. Our result shows that by combining the results from both audio and EEG models, a very simple neural network can surpass the performance of the single-modal approach.
Towards Universal Text-to-Speech

This paper studies a multilingual sequence-to-sequence text-to-speech framework towards universal modeling, that is able to synthesize speech for any speaker in any language using a single model. This framework consists of a transformer-based acoustic predictor and a WaveNet neural vocoder, with global conditions from speaker and language networks. It is examined on a massive TTS data set with around 1250 hours of data from 50 language locales, and the amount of data in different locales is highly unbalanced. Although the multilingual model exhibits the transfer learning ability to beneﬁt the low-resource languages, data imbalance still undermines the model performance. A data balance training strategy is successfully applied and effectively improves the voice quality of the low-resource languages. Furthermore, this paper examines the modeling capacity of extending to new speakers and languages, as a key step towards universal modeling. Experiments show 20 seconds of data is feasible for a new speaker and 6 minutes for a new language.
Determinants of knowledge on sexually transmitted infections among students in public higher education institutions in Melaka state, Malaysia

Introduction The increasing trend of sexually transmitted infections (STIs) among the young population is a significant public health problem. This study aimed to determine the level of knowledge on STIs among students in higher education institutions and its predicting factors, in Melaka. Methodology A cross-sectional study was conducted among 600 students from higher education institutions in Melaka aged between 18 to 30 years old. Multistage sampling of the institutions was performed. Valid and reliable self-administered questionnaire in the national language, Bahasa Malaysia, was used as to collect data on sociodemographic, personal background, knowledge on STIs and sources of information for STIs. Univariate, bivariate and multivariate analyses were conducted using IBM SPSS software version 25. Results The response rate for this study was 88%. The mean knowledge score was 24.1 ±5.1 out of 38. HIV was the most known STIs while gonorrhoea, trichomoniasis and chlamydial infections were among the least known STIs. Oral intercourse was the least known sexual activity that could transmit STIs. Higher proportion of respondents had correct knowledge on control and preventive measures of STIs (between 78% and 95%) compared to correct knowledge on sign and symptoms of STIs (between 8.5% and 67.8%). More than 90% of the respondents were unaware that a person infected with STIs could be symptom free. Four variables were identified as the determinants of the knowledge on STIs, which were level of education, place of stay, history of sexual and reproductive health education and involvement in STIs awareness programs (F (4,445) = 11.405, p <0.001, R2 = 0.093). Conclusions The knowledge on STIs among students in higher education institutions was unsatisfactory. The existing sexual education programs can be strengthened by delivering more information on other STIs rather than focusing on HIV only. The future program should focus on students of diploma and/or skill certificate and staying off-campus.
Extraction of antioxidant, phenolic content and mineral content from guava peel

Guava peel contains high amount of antioxidant activity and phenolic content and also the employment of peels would result in the waste to wealth application. The objective of this research is to investigate the extraction on antioxidant activity, phenolic content and mineral contents (Mg, Ca and Na) in guava peel. Guava peels were freeze dried and grounded before mixing it with different type of organic solvent (methanol, ethanol, acetone and ethyl acetate) in various solvent ratio (20, 40, 60, 80, 100%). After that, the extraction time (20, 40, 60, 80, 100, 120, 150, 180 minutes) and temperature (25, 35, 45, 55, 65 oC) were investigated to obtain the optimum condition for extraction. Then the extracts obtained were evaporated using rotary evaporator to remove solvent and were analyzed using Ultraviolet-Visible Spectrophotometer for analysis of antioxidant activity and phenolic content, Atomic Absorption Spectrophotometer for analysis of mineral content and High Performance Liquid Chromatography to verify ascorbic acid existence. It was found that methanol with 60% solvent/water ratio at temperature 55oC for 120 minute gave the highest yields of antioxidant activity and phenolic content which were 1021.00 μmol/L and 596.67mg/L. After the analysis using AAS, it was prove that peel of guava contained 206.65, 17.31 and 2.04 ppm of Mg, Ca and Na respectively. From analysis of HPLC, it was determined that there were significant amount of ascorbic acid in the extracts.
Whistled Vowel Identification by French Listeners

In this paper, we analyzed whistled vowel categorization by native French listeners. Whistled speech, a natural, yet modified register of speech, is used here as a tool to investigate perceptual processes in languages. We focused on four whistled vowels: /i, e, a, o/. After a detailed description of the vowels, we built and ran a behavioral experiment in which we asked native French speakers to categorize whistled vowel stimuli in which we introduced intraand interproduction variations. In addition, half of the participants performed the experiment in person (at the laboratory) while the other half participated online, allowing us to evaluate the impact of the testing set up. Our results confirm that the categorization rate of whistled vowels is above chance. They reveal significant differences in performance for different vowels and suggest an influence of certain acoustic parameters from the whistlers’ vowel range on categorization. Moreover, no effect or interaction was found for testing location and circumstances in our data set. This study confirms that whistled stimuli are a useful tool for studying how listeners process modified speech and which parameters impact sound categorization.
Mammalian orthoreovirus Infection is Enhanced in Cells Pre-Treated with Sodium Arsenite

Following reovirus infection, cells activate stress responses that repress canonical cellular translation as a mechanism to limit production of progeny virions. This includes the formation of stress granules (SG) that sequester translationally-stalled cellular transcripts, translation initiation factors, ribosomal proteins, and RNA binding proteins until conditions improve and translation can resume. Work by others suggests that these cellular stress responses, which are part of the integrated stress response, may benefit rather than repress reovirus replication. In agreement with this, we report that stressing cells prior to infection with sodium arsenite (SA), a robust inducer of SG and activator of eIF2α kinases, enhanced viral protein expression, percent infectivity and viral titer in SA-treated cells compared to untreated cells. SA-mediated enhancement of reovirus replication was not strain-specific, but was cell-type specific. While pre-treatment of cells with SA offered the greatest enhancement, treatment of infected cultures as late as 4 h post infection resulted in an increase in the percent of cells infected. SA activates the HRI kinase, which phosphorylates eIF2α and subsequently induces SG formation. Other stresses, such as heat shock (HS) and osmotic shock also activate HRI. Heat shock of cells prior to reovirus infection readily induced SG in greater than 85% of cells. Although HS pre-treatment had no effect on the percentage of infected cells or viral yield, it did enhance viral protein expression. These data suggest that SA pre-treatment perturbs the cell in a way that is beneficial for reovirus and that neither HRI activation nor SG induction is sufficient for reovirus infection enhancement. SIGNIFICANCE All viruses rely on the host translational machinery for the synthesis of viral proteins. In response to viral infection, cells activate the integrated stress response resulting in the phosphorylation of eIF2α and translation shutoff. Despite this, reovirus replicates to reduced titers in the absence of this response. In this work, we report that sodium arsenite activation of the integrated stress response prior to virus inoculation enhances virus infectivity, protein expression and titer. Together, these data suggest that modulation of conserved cellular stress responses can alter reovirus replication.
Fast and Slow Acoustic Model

In this work we layout a Fast & Slow (F&S) acoustic model (AM) in an encoder-decoder architecture for streaming automatic speech recognition (ASR). The Slow model represents our baseline ASR model; it’s signiﬁcantly larger than Fast model and provides stronger accuracy. The Fast model is generally developed for related speech applications. It has weaker ASR accuracy but is faster to evaluate and consequently leads to better user-perceived latency. We propose a joint F&S model that encodes output state information from Fast model, feeds that to Slow model to improve overall model accuracy from F&S AM. We demonstrate scenarios where individual Fast and Slow models are already available to build the joint F&S model. We apply our work on a large vocabulary ASR task. Compared to Slow AM, our Fast AM is 3-4x smaller and 11.5% relatively weaker in ASR accuracy. The proposed F&S AM achieves 4.7% relative gain over the Slow AM. We also report a progression of techniques and improve the relative gain to 8.1% by encoding additional Fast AM outputs. Our proposed framework has generic attributes - we demonstrate a speciﬁc extension by encoding two Slow models to achieve 12.2% relative gain.
Paralinguistic Classification of Mask Wearing by Image Classifiers and Fusion

In this study, we address the ComParE 2020 Paralinguistics Mask sub-challenge, where the task is the detection of wearing surgical masks from short speech segments. In our approach, we propose a computer-vision-based pipeline to utilize the capabilities of deep convolutional neural network-based image classifiers developed in recent years and apply this technology to a specific class of spectrograms. Several linear and logarithmic scale spectrograms were tested, and the best performance is achieved on linear-scale, 3-Channel Spectrograms created from the audio segments. A single model image classifier provided a 6.1% better result than the best single-dataset baseline model. The ensemble of our models further improves accuracy and achieves 73.0% UAR by training just on the ‘train’ dataset and reaches 80.1% UAR on the test set when training includes the ‘devel’ dataset, which result is 8.3% higher than the baseline. We also provide an activation-mapping analysis to identify frequency ranges that are critical in the ‘mask’ versus ‘clear’ classification.
Teaching autonomy: does Spanish education system achieve the desired effect?

espanolA pesar de que el curriculo espanol para la educacion primaria establece asignaturas troncales y especificas para promover la autonomia docente, la gestion curricular esta logrando el efecto contrario segun el profesorado. El objetivo de este estudio es comparar estadisticamente la autonomia de maestras y maestros que imparten asignaturas troncales y de quienes ensenan solo asignaturas especificas. El metodo comprende un diseno cuantitativo no experimental, estableciendo una muestra aleatoria estratificada que tiene representatividad nacional. Los resultados demuestran que el profesorado que solo imparte asignaturas especificas percibe mayor satisfaccion hacia la actividad pedagogica que el otro grupo. En conclusion, es razonable poner bajo escrutinio el curriculo espanol porque su efectividad para fomentar la autonomia docente queda cuestionada. EnglishDespite the Spanish curriculum for primary education establishes compulsory and elective subjects in order to promote teacher autonomy; curricular management is achieving the opposite of the desired effect according to teachers. This study aims statistically comparing the autonomy of educators who teach compulsory subjects and who only impart elective subjects. The method comprises a nonexperimental quantitative research design, establishing a random cluster sampling that has statistical representativeness from national standpoint. Results demonstrate that teachers who only impart elective subjects perceive a greater enjoyment toward pedagogical activity than the other group. In conclusion, it is reasonable to put the Spanish curriculum under scrutiny because its effectiveness for fostering the teacher autonomy is questioned.
Large-Scale End-to-End Multilingual Speech Recognition and Language Identification with Multi-Task Learning

In this paper, we report a large-scale end-to-end language-independent multilingual model for joint automatic speech recognition (ASR) and language identiﬁcation (LID). This model adopts hybrid CTC/attention architecture and achieves word error rate (WER) of 52.8 and LID accuracy of 93.5 on 42 languages with around 5000 hours of training data. We also compare the effects of using subword-level or character-level vocabulary for large-scale multilingual tasks. Furthermore, we transfer the pre-trained model to 14 low-resource languages. Results show that the pre-trained model achieves signiﬁcantly better results than non-pretrained baselines on both language-speciﬁc and multilingual low-resource ASR tasks in terms of WER, which is reduced by 28.1% and 11.4% respectively.