Miracle-dz/UniFAR-FTUs huggingface dataset:
Spectral Moment and Duration of Burst of Plosives in Speech of Children with Hearing Impairment and Typically Developing Children - A Comparative Study

Speech development in children with hearing impairment (CHI) is hampered by inadequate auditory input. Speech of CHI has reduced intelligibility compared to typically developing children (TDC), mainly because of articulatory errors. Speech language pathologists (SLPs) assess these errors through perceptual evaluation and accordingly device the protocol to correct them through several sessions of speech therapy. Automatic methods need to be developed to reduce the time and enhance the accuracy of assessment. Acoustic measures of plosives may be utilized as valuable cues for automatic assessment. The current study was aimed to investigate the burst duration and spectral moment (centroid, skewness and kurtosis) of plosives in CHI in comparison with TDC. 24 children in the age range of 5 to 8 years, divided into group I (13 TDC) and group II (11 CHI) participated. Six words in Hindi embedded with plosives (/p/, /b/, /ʈ/, /ɖ/, /k/, /ɡ/) in the initial position were used as speech material. Burst duration, spectral centroid and skewness were found to be significantly different across the groups for most of the plosives, whereas kurtosis was not. Results indicate that these measures except kurtosis are potential cues for automatic assessment of articulatory errors.
A Noise Robust Technique for Detecting Vowels in Speech Signals

In this work, we propose a novel and noise robust method for the detection of vowels in speech signals. The proposed approach combines variational mode decomposition (VMD) and non-local means (NLM) estimation for the detection of vowels in a speech sequence. The VMD algorithm is used to determine a number of variational mode functions (VMFs). The lower-order VMFs represent the frequency contents corresponding to vowel regions. Thus by combining the lower-order VMFs and reconstructing the speech signal back, the energy corresponding to the vowel regions is enhanced while the non-vowel regions are suppressed. At the same time, the ill-effect of noise is also reduced. Finally, as reported in an earlier work, application of NLM followed by convolution with ﬁrst-order difference of Gaussian window is performed on the reconstructed signal to determine the vowel region. The performance of proposed approach for the task of detecting vowels in speech is compared with three existing techniques and observed to be superior under clean as well as noisy test conditions.
Training Speaker Enrollment Models by Network Optimization

In this paper, we present a new approach for the enrollment process in a deep neural network (DNN) system which learns the speaker model by an optimization process. Most Speaker Ver-iﬁcation (SV) systems extract representations for both the enrollment and test utterances called embeddings, and then, these systems usually apply a similarity metric or complex back-ends to carry out the veriﬁcation process. Unlike previous works, we propose to take advantage of the knowledge acquired by a DNN to model the speakers from the training set since the last layer of the DNN can be seen as an embedding dictionary which represents train speakers. Thus, after the initial training phase, we introduce a new learnable vector for each enrollment speaker. Furthermore, to lead this training process, we employ a loss function more appropriate for veriﬁcation, the approximated Detection Cost Function ( aDCF ) loss function. The new strategy to produce enrollment models for each target speaker was tested on the RSR-Part II database for text-dependent speaker veriﬁcation, where the proposed approach outperforms the reference system based on directly averaging of the embeddings extracted from the enroll data using the network and the application of cosine similarity.
End-to-End Speech Emotion Recognition Combined with Acoustic-to-Word ASR Model

In this paper, we propose speech emotion recognition (SER) combined with an acoustic-to-word automatic speech recognition (ASR) model. While acoustic prosodic features are primarily used for SER, textual features are also useful but are error-prone, especially in emotional speech. To solve this problem, we integrate ASR model and SER model in an end-to-end manner. This is done by using an acoustic-to-word model. Specifically, we utilize the states of the decoder in the ASR model with the acoustic features and input them into the SER model. On top of a recurrent network to learn features from this input, we adopt a self-attention mechanism to focus on important feature frames. Finally, we ﬁnetune the ASR model on the new dataset using a multi-task learning method to jointly optimize ASR with the SER task. Our model has achieved a 68.63% weighted accuracy (WA) and 69.67% unweighted accuracy (UA) on the IEMOCAP database, which is state-of-the-art performance.
An NMF-HMM Speech Enhancement Method Based on Kullback-Leibler Divergence

In this paper, we present a novel supervised Non-negative Matrix Factorization (NMF) speech enhancement method, which is based on Hidden Markov Model (HMM) and Kullback-Leibler (KL) divergence (NMF-HMM). Our algorithm applies the HMM to capture the timing information, so the temporal dynamics of speech signal can be considered by comparing with the traditional NMF-based speech enhancement method. More speciﬁcally, the sum of Poisson, leading to the KL divergence measure, is used as the observation model for each state of HMM. This ensures that the parameter update rule of the proposed algorithm is identical to the multiplicative update rule, which is quick and efﬁcient. In the training stage, this update rule is applied to train the NMF-HMM model. In the online enhancement stage, a novel minimum mean-square error (MMSE) estimator that combines the NMF-HMM is proposed to conduct speech enhancement. The performance of the proposed algorithm is evaluated by perceptual evaluation of speech quality (PESQ) and short-timeobjective intelligibility (STOI). The experimental results indicate that the STOI score of proposed strategy is able to outperform 7% than current state-of-the-art NMF-based speech enhancement methods.
Data Augmentation for Code-Switch Language Modeling by Fusing Multiple Text Generation Methods

To deal with the problem of data scarce in training language model (LM) for code-switching (CS) speech recognition, we proposed an approach to obtain augmentation texts from three different viewpoints. The ﬁrst one is to enhance monolingual LM by selecting corresponding sentences for existing conversational corpora; The second one is based on replacements using syntactic constraint for a monolingual Chinese corpus, with the helps of an aligned word list obtained from a pseudo-parallel corpus, and part-of-speech (POS) of words; The third one is to use text generation based on a pointer-generator network with copy mechanism, using a real CS text data for training. All sentences from these approaches show improvement for CS LMs, and they are ﬁnally fused into an LM for CS ASR tasks. Evaluations on LMs built by the above augmented data were conducted on two Mandarin-English CS speech sets D-TANG, and SEAME. The perplexities were greatly reduced with all kinds of augmented texts, and speech recognition performances were steadily improved. The mixed word error rate (MER) of DTANG and SEAME evaluation dataset got relative reduction by 9.10% and 29.73%, respectively.
Speaker and Phoneme-Aware Speech Bandwidth Extension with Residual Dual-Path Network

Speech bandwidth extension aims to generate a wideband signal from a narrowband (low-band) input by predicting the missing high-frequency components. It is believed that the general knowledge about the speaker and phonetic content strengthens the prediction. In this paper, we propose to augment the low-band acoustic features with i-vector and phonetic posteri-orgram (PPG), which represent speaker and phonetic content of the speech, respectively. We also propose a residual dual-path network (RDPN) as the core module to process the augmented features, which fully utilizes the utterance-level temporal continuity information and avoids gradient vanishing. Experiments show that the proposed method achieves 20.2% and 7.0% relative improvements over the best baseline in terms of log-spectral distortion (LSD) and signal-to-noise ratio (SNR), respectively. Furthermore, our method is 16 times more compact than the best baseline in terms of the number of parameters.
FusionRNN: Shared Neural Parameters for Multi-Channel Distant Speech Recognition

Distant speech recognition remains a challenging application for modern deep learning based Automatic Speech Recognition (ASR) systems, due to complex recording conditions involving noise and reverberation. Multiple microphones are commonly combined with well-known speech processing techniques to enhance the original signals and thus enhance the speech recognizer performance. These multi-channel follow similar input distributions with respect to the global speech information but also contain an important part of noise. Consequently, the input representation robustness is key to obtaining reasonable recognition rates. In this work, we propose a Fusion Layer (FL) based on shared neural parameters. We use it to produce an expressive embedding of multiple microphone signals, that can easily be combined with any existing ASR pipeline. The proposed model called FusionRNN showed promising results on a multi-channel distant speech recognition task, and consistently outperformed baseline models while maintaining an equal training time.
UncommonVoice: A Crowdsourced Dataset of Dysphonic Speech

To facilitate more accessible spoken language technologies and advance the study of dysphonic speech this paper presents Un-commonVoice, a freely-available, crowd-sourced speech corpus consisting of 8.5 hours of speech from 57 individuals, 48 of whom have spasmodic dysphonia. The speech material consists of non-words (prolonged vowels, and the prompt for di-adochokinetic rate), sentences (randomly selected from TIMIT prompts and the CAPE-V intelligibility analysis), and sponta-neous image descriptions. The data was recorded in a crowd-sourced manner using a web-based application. This dataset is a fundamental resource for the development of voice-assistive technologies for individuals with dysphonia as well as the enhancement of the accessibility of voice-based technologies (automatic speech recognition, virtual assistants, etc). Research on articulation differences as well as how best to model and represent dysphonic speech will greatly beneﬁt from a free and publicly available dataset of dysphonic speech. The dataset will be made available at http://uncommonvoice.org . In the following sections, we detail the data collection process as well as provide an initial analysis of the speech corpus.
Autoencoder Bottleneck Features with Multi-Task Optimisation for Improved Continuous Dysarthric Speech Recognition

Automatic recognition of dysarthric speech is a very challenging research problem where performances still lag far behind those achieved for typical speech. The main reason is the lack of suitable training data to accommodate for the large mismatch seen between dysarthric and typical speech. Only recently has focus moved from single-word tasks to exploring continuous speech ASR needed for dictation and most voice-enabled interfaces. This paper investigates improvements to dysarthric continuous ASR. In particular, we demonstrate the effectiveness of using unsupervised autoencoder-based bottleneck (AEBN) feature extractor trained on out-of-domain (OOD) LibriSpeech data. We further explore multi-task optimisation techniques shown to benefit typical speech ASR. We propose a 5fold cross-training setup on the widely used TORGO dysarthric database. A setup we believe is more suitable for this lowresource data domain. Results show that adding the proposed AE-BN features achieves an average absolute (word error rate) WER improvement of 2.63% compared to the baseline system. A further reduction of 2.33% and 0.65% absolute WER is seen when applying monophone regularisation and joint optimisation techniques, respectively. In general, the ASR system employing monophone regularisation trained on AE-BN features exhibits the best performance.
Does Lexical Retrieval Deteriorate in Patients with Mild Cognitive Impairment? Analysis of Brain Functional Network Will Tell

Alterations in speech and language are typical signs of mild cognitive impairment (MCI), considered to be the prodromal stage of Alzheimer’s disease (AD). Yet, very few studies have pointed out at what stage their speech production is disrupted. To bridge this knowledge gap, the present study focused on lexical retrieval, a specific process during speech production, and investigated how it is affected in cognitively impairment patients with the state-of-the-art analysis of brain functional network. 17 patients with MCI and 20 age-matched controls were invited to complete a primed picture naming task, of which the prime was either semantically related or unrelated to the target. Using electroencephalography (EEG) signals collected during task performance, even-related potentials (ERPs) were analyzed, together with the construction of the brain functional network. Results showed that whereas MCI patients did not exhibit significant differences in reaction time and ERP responses, their brain functional network did alter associated with a significant main effect in accuracy. The observation of increased cluster coefficients and characteristic path length indicated deteriorations in global information processing, which provided evidence that deficits in lexical retrieval might have occurred even at the preclinical stage of AD.
Shredders prefer soft and fungal-conditioned leaves, regardless of their initial chemical traits

ABSTRACT Through field and laboratory experiments we investigated the effects of leaf traits of two tree species and microbial conditioning on the abundance, biomass, and feeding preference of a typical macroinvertebrate shredder. In the field, we compared the association of Phylloicus (Calamoceratidae, Trichoptera) with two tree species commonly found in riparian zones, which are representative of high and low nutritional quality, respectively: Nectandra megapotamica and Chusquea tenella. In the laboratory, we investigated the feeding preference of Phylloicus using unconditioned leaves and leaves conditioned by aquatic fungi. The same tree species used in the field experiment were used in the laboratory. Initially, C. tenella leaves were proved to be more nutritious and softer, while N. megapotamica leaves were harder and more lignified. The shredders preferred conditioned leaf detritus of reduced toughness (field: C. tenella; laboratory: N. megapotamica, both conditioned for 14 days). These leaf traits seem to be crucial for the choice process of Phylloicus. After 14 days, N. megapotamica leaves showed a decreased toughness associated with the microbial conditioning, which explained its consumption rate by Phylloicus. In both field and laboratory experiments, we found evidence that Phylloicus is a selective feeding shredder, and that the leaf traits, especially leaf structure (e.g., leaf toughness and lignin content), determine its association and preferences.
The Acoustic Realization of Mandarin Tones in Fast Speech

Many studies have demonstrated that acoustic contrasts between speech segments (vowels and consonants) were reduced when speaking rate increases, while it was unclear whether tones in tonal languages also undergo similar modifications. Mandarin Chinese is a tonal language, while results regarding the rate effect on Mandarin tones in previous studies were mixed, probably driven by the material difference, i.e., the position of target tones within a sentence. Therefore, the present study examined the effect of speaking rate on Mandarin tones, comparing the pitch contour and tonal contrast of Mandarin tones between normal and fast speech across utterance initial, medial and final positions. The results showed that, relative to normal speech, lexical tones in Mandarin Chinese exhibited overall higher and flatter pitch contours, with smaller tonal space. Moreover, the rate effect on tones did not vary with position. The current results and previous studies on segments thus revealed a universal pattern of speech reduction in fast speech at both segmental and suprasegmental levels.
University of Birmingham Heterogeneity of germline variants in high risk breast and ovarian cancer susceptibility genes in India

Breast and ovarian cancers now account for one in three cancers in Indian women and their incidence is rising. Major differences in the clinical presentation of breast and ovarian cancers exist between India and the United Kingdom. For example, Indian patients with breast cancer typically present a decade earlier than in the UK. Reasons for this could be multifactorial, including differences in underlying biology, environmental risks, and other systematic factors including access to screening. One possible explanation lies in variable incidence or penetrance of germline mutations in genes such as BRCA1 and BRCA2. We performed a methodical database and literature review to investigate the prevalence and spectrum of high-risk cancer susceptibility genes in Indian patients with breast and ovarian cancers. We identi ﬁ ed 148 articles, but most studies were small, with inconsistent inclusion criteria and based on heterogeneous technologies, so that mutation frequency could not be reliably ascertained. Data were also often lacking on penetrance, histopathology, and survival outcomes. After ﬁ ltering out unsuitable studies, only 13 remained, comprising 1028 patients. Large-scale research studies are urgently needed to determine mutation prevalence, spectra, and clinico-pathological fea-tures, and hence derive guidelines for screening, treatment, and prevention speci ﬁ c to the Indian population.
COVID-19 and the Acceleration of Digital Transformation and the Future of Work

ABSTRACT The restrictions introduced by COVID-19 forced firms to adapt to a technology-intensive operational model. These digital transformations involved deliberations among stakeholders to adjust strategy and general functionally of companies, which included elements of the future of work. This paper leverages existing research, and input from firms in various industries to illustrate technology-based issues including elements of the future of work that are transpiring in organizations as they adapt to this disruptive environment.
Women, War and Sport: The Battle of the 2019 Solheim Cup

One of the most significant and/or prevalent symbols of nationhood is to be found in the international (men’s) sporting arena. Sport is often imbued with notions of national identity and war, although the sport of golf is generally devoid of flags and nationalistic tendencies and is thus often considered relatively insignificant in inculcating national sentiments – except in the exceptional cases of team golf events such as the Solheim Cup. This paper considers the way in which the competitors in the 2019 Solheim Cup were represented in the British print media. Results highlights that national identity is a key descriptor of the female competitors, legitimising their position in the battlefield of international sport. We conclude that, in an era of increasing significance of women’s sport, there exists an ideological space for women to be seen as ‘proxy warriors’ in sport.
Primary cilia as the nexus of biophysical and hedgehog signaling at the tendon enthesis

Primary cilia at the tendon enthesis serve as mechanosensors and Hh signal transducers. The tendon enthesis is a fibrocartilaginous tissue critical for transfer of muscle forces to bone. Enthesis pathologies are common, and surgical repair of tendon to bone is plagued by high failure rates. At the root of these failures is a gap in knowledge of how the tendon enthesis is formed and maintained. We tested the hypothesis that the primary cilium is a hub for transducing biophysical and hedgehog (Hh) signals to regulate tendon enthesis formation and adaptation to loading. Primary cilia were necessary for enthesis development, and cilia assembly was coincident with Hh signaling and enthesis mineralization. Cilia responded inversely to loading; increased loading led to decreased cilia and decreased loading led to increased cilia. Enthesis responses to loading were dependent on Hh signaling through cilia. Results imply a role for tendon enthesis primary cilia as mechanical responders and Hh signal transducers, providing a therapeutic target for tendon enthesis pathologies.
Overexcitability Profile Among University Students at Music-Focused Institutions

ABSTRACT Overexcitability (OE) is a key concept introduced in the literature by Kazimierz Dąbrowski who distinguished 5 forms of OE (psychomotor, sensual, imaginational, intellectual, and emotional) and showed that they are correlated with outstanding talents and creativity. In this study a group of musically talented individuals (n = 106) was compared with a control group (n = 106). OEQ-II was used to measure OE. Female musicians scored significantly higher in sensual, imaginational, and intellectual OEs compared to the women from the control group, while male musicians scored significantly higher in sensual and emotional OEs and lower in psychomotor OE compared to the men from the control group. In addition, the musicians and non-musicians were compared with the sten norms for OEQ-II. It was found that the number of individuals showing high emotional and high sensual OEs was more than twice as high in the group of musicians than in the control group.
ACSL3–PAI-1 signaling axis mediates tumor-stroma cross-talk promoting pancreatic cancer progression

Pharmacological inhibition of PAI-1 strongly enhances chemo- and immunotherapeutic response against PDAC. Pancreatic ductal adenocarcinoma (PDAC) is characterized by marked fibrosis and low immunogenicity, features that are linked to treatment resistance and poor clinical outcomes. Therefore, understanding how PDAC regulates the desmoplastic and immune stromal components is of great clinical importance. We found that acyl-CoA synthetase long-chain 3 (ACSL3) is up-regulated in PDAC and correlates with increased fibrosis. Our in vivo results show that Acsl3 knockout hinders PDAC progression, markedly reduces tumor fibrosis and tumor-infiltrating immunosuppressive cells, and increases cytotoxic T cell infiltration. This effect is, at least in part, due to decreased plasminogen activator inhibitor–1 (PAI-1) secretion from tumor cells. Accordingly, PAI-1 expression in PDAC positively correlates with markers of fibrosis and immunosuppression and predicts poor patient survival. We found that PAI-1 pharmacological inhibition strongly enhances chemo- and immunotherapeutic response against PDAC, increasing survival of mice. Thus, our results unveil ACSL3–PAI-1 signaling as a requirement for PDAC progression with druggable attributes.