Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding the Reproductive System of the Olive Fruit Fly, Bactrocera oleae

A great deal of behavioral and molecular interactions between male and female insects takes place during insect reproduction. Here, we comprehensively analyze the reproductive system of the olive fruit fly. Specifically, transcriptomic and genomic analysis of the reproductive tissues from virgin and once mated insects were performed. Genes encoding proteins implicated in immune response, mucins, antigen 5 proteins, proteases inhibitors and proteins with putative secretory activity were identified. Comparison of the transcriptomes between virgin and mated insects resulted in the identification of genes that are up- or down-regulated after mating. In testes 106 genes were up-regulated and 344 genes were down-regulated, whereas in male accessory glands with ejaculatory bulb 1,607 genes were up-regulated and 384 genes were down-regulated in mated male insects. Respectively, in mated females 1,705 genes were up-regulated and 120 genes were down-regulated in mated insects. To get a deeper insight, the expression profiles of selected genes throughout sexual maturation for the male tissues and throughout different time points after mating for the female reproductive tissues were determined. Identification of genes that take part in the mating procedure not only gives an insight in the biology of the insects, but it could also help the identification of new target genes in order to disturb the reproductive success of the olive fly and thus develop alternative pest control method.
Mixed Case Contextual ASR Using Capitalization Masks

End-to-end (E2E) mixed-case automatic speech recognition (ASR) systems that directly predict words in the written domain are attractive due to being simple to build, not requiring explicit capitalization models, allowing streaming capitalization without additional effort beyond that required for streaming ASR, and their small size. However, the fact that these systems produce various versions of the same word with different cap-italizations, and even different word segmentations for different case variants when wordpieces (WP) are predicted, leads to multiple problems with contextual ASR. In particular, the size of and time to build contextual models grows considerably with the number of variants per word. In this paper, we propose sep-arating orthographic recognition from capitalization, so that the ASR system ﬁrst predicts a word, then predicts its capitalization in the form of a capitalization mask. We show that the use of capitalization masks achieves the same low error rate as traditional mixed-case ASR, while reducing the size and compilation time of contextual models. Furthermore, we observe signiﬁcant improvements in capitalization quality.
Mixtures of Deep Neural Experts for Automated Speech Scoring

The paper copes with the task of automatic assessment of second language proficiency from the language learners' spoken responses to test prompts. The task has significant relevance to the field of computer assisted language learning. The approach presented in the paper relies on two separate modules: (1) an automatic speech recognition system that yields text transcripts of the spoken interactions involved, and (2) a multiple classifier system based on deep learners that ranks the transcripts into proficiency classes. Different deep neural network architectures (both feed-forward and recurrent) are specialized over diverse representations of the texts in terms of: a reference grammar, the outcome of probabilistic language models, several word embeddings, and two bag-of-word models. Combination of the individual classifiers is realized either via a probabilistic pseudo-joint model, or via a neural mixture of experts. Using the data of the third Spoken CALL Shared Task challenge, the highest values to date were obtained in terms of three popular evaluation metrics.
Prosody and Breathing: A Comparison Between Rhetorical and Information-Seeking Questions in German and Brazilian Portuguese

Several studies have shown that rhetorical wh -questions (RQs) and string-identical information-seeking wh -questions (ISQs) are realized with different prosodic characteristics. In contrast to ISQs, RQs have been shown to be phonetically realized with a breathier (i.e., softer) voice quality (e.g., German and English) and longer constituent durations (e.g., German, English, Icelandic). Based on similar results found for different languages, we investigate wh -RQs and sting-identical wh -ISQs in Brazilian Portuguese (BP) and German (G). We analyze (i) whether specific duration and voice-quality patterns characterize and separate the two illocution types (RQ and ISQ) in BP, and (ii) if direct measures of the respiratory sub-system reveal differences between illocution types, given that breathiness involves greater transglottal air flow which can be observed in the speakers’ chest and/or abdomen movement. Our data suggest that, similar to G, English, and Icelandic, duration and voice quality patterns play a role in the realization of RQs compared to ISQs in BP, reinforcing the assumption that there are cross-linguistically similar phonetic features in the realization of RQs compared to ISQs. We also find that speakers of G breathe in more deeply and dynamically than speakers of BP, suggesting a link between breathing and voice quality.
Deep Embedding Learning for Text-Dependent Speaker Verification

In this paper we present an effective deep embedding learning architecture for speaker veri ﬁ cation task. Compared with the widely used residual neural network (ResNet) and time-delay neural network (TDNN) based architectures, two main improvements are proposed: 1) We use densely connected convolutional network (DenseNet) to encode the short term context information of the speaker. 2) A bidirectional attentive pooling strategy is proposed to further model the long term temporal context and aggregate the important frames which re ﬂ ect the speaker identity. We evaluate the proposed architecture on the task of text-dependent speaker veri ﬁ cation in the Interspeech 2020 Far Field Speaker Veri ﬁ cation Challenge (FFSVC2020). Result shows that the proposed algorithm outperforms the of ﬁ - cial baseline of FFSVC2020 with 8.06%, 19.70% minDCFs and 9.26%, 16.16% EERs relative reductions on the evaluation set of Task 1 and Task 3 respectively.
Parkinson's Disease Detection from Speech Using Single Frequency Filtering Cepstral Coefficients

Parkinson’s disease (PD) is a progressive deterioration of the human central nervous system. Detection of PD (discriminating patients with PD from healthy subjects) from speech is a useful approach due to its non-invasive nature. This study proposes to use novel cepstral coefﬁcients derived from the single frequency ﬁltering (SFF) method, called as single frequency ﬁl-tering cepstral coefﬁcients (SFFCCs) for the detection of PD. SFF has been shown to provide higher spectro-temporal resolution compared to the short-time Fourier transform. The current study uses the PC-GITA database, which consists of speech from speakers with PD and healthy controls (50 males, 50 females). Our proposed detection system is based on the i-vectors derived from SFFCCs using SVM as a classiﬁer. In the detection of PD, better performance was achieved when the i-vectors were computed from the proposed SFFCCs compared to the popular conventional MFCCs. Furthermore, we investigated the effect of temporal variations by deriving the shifted delta cepstral (SDC) coefﬁcients using SFFCCs. These experiments revealed that the i-vectors derived from the proposed SF-FCCs+SDC features gave an absolute improvement of 9% compared to the i-vectors derived from the baseline MFCCs+SDC features, indicating the importance of temporal variations in the detection of PD.
Building a Robust Word-Level Wakeword Verification Network

Wakeword detection is responsible for switching on down-stream systems in a voice-activated device. To prevent a response when the wakeword is detected by mistake, a secondary network is often utilized to verify the detected wakeword. Published veriﬁcation approaches are formulated based on Automatic Speech Recognition (ASR) biased towards the wakeword. This approach has several drawbacks, including high model complexity and the necessity of large vocabulary training data. To address these shortcomings, we propose to use a large receptive ﬁeld (LRF) word-level wakeword model, and in particular, a convolutional-recurrent-attention (CRA) network. CRA networks use a strided small receptive ﬁeld convolutional front-end followed by ﬁxed time-step recurrent layers optimized to model the temporal phonetic dependencies within the wakeword. We experimentally show that this type of modeling helps the sys-tem to be robust to errors in the location of the wakeword as estimated by the detection network. The proposed CRA network signiﬁcantly outperforms previous baselines, including an LRF whole-word convolutional network and a 2-stage DNN-HMM system. Additionally, we study the importance of pre-and post-wakeword context. Finally, the CRA network has signiﬁcantly fewer model parameters and multiplies, which makes it suitable for real-world production applications.
Multilingual Speech Recognition with Self-Attention Structured Parameterization

Multilingual automatic speech recognition systems can transcribe utterances from different languages. These systems are attractive from different perspectives: they can provide quality improvements, specially for lower resource languages, and simplify the training and deployment procedure. End-to-end speech recognition has further simpliﬁed multilingual modeling as one model, instead of several components of a classical sys-tem, have to be uniﬁed. In this paper, we investigate a stream-able end-to-end multilingual system based on the Transformer Transducer [1]. We propose several techniques for adapting the self-attention architecture based on the language id. We analyze the trade-offs of each method with regards to quality gains and number of additional parameters introduced. We conduct experiments in a real-world task consisting of ﬁve languages. Our experimental results demonstrate ∼ 8% to ∼ 20% relative gain over the baseline multilingual model.
Joint Training for Simultaneous Speech Denoising and Dereverberation with Deep Embedding Representations

Monaural speech dereverberation is a very challenging task because no spatial cues can be used. When the additive noises exist, this task becomes more challenging. In this paper, we propose a joint training method for simultaneous speech de-noising and dereverberation using deep embedding representations. Firstly, at the denoising stage, the deep clustering (DC) network is used to extract noise-free deep embedding representations from the anechoic speech and residual reverberation signals. These deep embedding representations are represent the inferred spectral masking patterns of the desired signals so that they could discriminate the anechoic speech and the reverberant signals very well. Secondly, at the dereverberation stage, we utilize another supervised neural network to estimate the mask of anechoic speech from these deep embedding representations. Finally, the joint training algorithm is used to train the speech denoising and dereverberation network. Therefore, the noise reduction and dereverberation can be simultaneously optimized. Our experiments are conducted on the TIMIT dataset. Experimental results show that the proposed method outperforms the WPE and BLSTM baselines. Especially in the low SNR (-5 dB) condition, our proposed method produces a relative improvement of 7.8% for PESQ compared with BLSTM method and relative reductions of 16.3% and 19.3% for CD and LLR measures.
A Space-and-Speaker-Aware Iterative Mask Estimation Approach to Multi-Channel Speech Recognition in the CHiME-6 Challenge

We propose a space-and-speaker-aware iterative mask estimation (SSA-IME) approach to improving complex angular central Gaussian distributions (cACGMM) based beamforming in an iterative manner by leveraging upon the complementary information obtained from SSA-based regression. First, a mask calculated by beamformed speech features is proposed to enhance the estimation accuracy of the ideal ratio mask from noisy speech. Second, the outputs of cACGMM-beamformed speech with given time annotation as initial values are used to extract the log-power spectral and inter-phase difference features of different speakers serving as inputs to estimate the regression-based SSA model. Finally, in decoding, the mask estimated by the SSA model is also used to iteratively reﬁne cACGMM-based masks, yielding enhanced multi-array speech. Tested on the recent CHiME-6 Challenge Track 1 tasks, the proposed SSA-IME framework signiﬁcantly and consistently out-performs state-of-the-art approaches, and achieves the lowest word error rates for both Track 1 speech recognition tasks.
Larger fragments have more late‐successional species of woody plants than smaller fragments after 50 years of secondary succession

Most fragmentation research focuses on the effects of carving up old‐growth forests, but less is known about influences of habitat fragmentation on secondary succession in patches of regenerating forests. Working with forest dynamics on islands in a vast lake created by a hydroelectric dam in China (the Thousand Island Lake), we sampled 29 islands that were cleared of forest during dam construction in 1959 and then underwent succession. Measurements taken in 2009–2010 and 2014–2015 evaluated community assembly during succession, based on species diversity, functional traits and structural properties. Forests on small islands remained at relatively early stages of compositional succession: species richness was low, and communities were dominated by early‐successional species, with few animal‐dispersed and shade‐tolerant plants. However, these islands had accumulated similar above‐ground biomass density to that on larger islands, mostly in the stems of a fast‐growing pine species. Island size was the key driver of secondary succession in regenerating forest fragments, while isolation had comparatively little effect. Edge effects were important for species composition and functional composition. Synthesis. Habitat fragmentation resulting from the creation of an artificial lake affected community assembly, with fewer late‐successional species on smaller islands. Maintenance of large fragments is critical for the rapid succession of forests. Paradigms on habitat fragmentation effects drawn from old‐growth forest studies are unlikely to hold in regenerating forest fragments. Restoration activities should consider landscape patterns to accelerate secondary succession of regenerating forests.
Emitting Word Timings with End-to-End Models

Having end-to-end (E2E) models emit the start and end times of words on-device is important for various applications. This un-solved problem presents challenges with respect to model size, latency and accuracy. In this paper, we present an approach to word timings by constraining the attention head of the Listen, Attend, Spell (LAS) 2nd-pass rescorer [1]. On a Voice-Search task, we show that this approach does not degrade accuracy compared to when no attention head is constrained. In addition, it meets on-device size and latency constraints. In comparison, constraining the alignment with a 1st-pass Recurrent Neural Network Transducer (RNN-T) model to emit word timings results in quality degradation. Furthermore, a low-frame-rate conventional acoustic model [2], which is trained with a constrained alignment and is used in many applications for word timings, is slower to detect start and end times compared to our proposed 2nd-pass LAS approach.
Simulating Realistically-Spatialised Simultaneous Speech Using Video-Driven Speaker Detection and the CHiME-5 Dataset

Simulated data plays a crucial role in the development and evaluation of novel distant microphone ASR techniques. However, the commonly used simulated datasets adopt uninformed and potentially unrealistic speaker location distributions. We wish to generate more realistic simulations driven by recorded human behaviour. By using devices with a paired microphone array and camera, we analyse unscripted dinner party scenarios (CHiME-5) to estimate the distribution of speaker separation in a realistic setting. We deploy face-detection, and pose-detection techniques on 114 cameras to automatically locate speakers in 20 dinner party sessions. Our analysis found that on average, the separation between speakers was only 17 degrees. We use this analysis to create datasets with realistic distributions and compare it with commonly used datasets of simulated signals. By changing the position of speakers, we show that the word error rate can increase by over 73.5% relative when using a strong speech enhancement and ASR system.
Nonlinear Residual Echo Suppression Using a Recurrent Neural Network

The acoustic front-end of hands-free communication devices introduces a variety of distortions to the linear echo path between the loudspeaker and the microphone. While the ampli-ﬁers may introduce a memory-less non-linearity, mechanical vibrations transmitted from the loudspeaker to the microphone via the housing of the device introduce non-linarities with memory, which are much harder to compensate. These distortions significantly limit the performance of linear Acoustic Echo Cancellation (AEC) algorithms. While there already exists a wide range of Residual Echo Suppressor (RES) techniques for individual use cases, our contribution speciﬁcally aims at a low-resource implementation that is also real-time capable. The proposed approach is based on a small Recurrent Neural Network (RNN) which adds memory to the residual echo suppressor, enabling it to compensate both types of non-linear distortions. We evaluate the performance of our system in terms of Echo Return Loss En-hancement (ERLE), Signal to Distortion Ratio (SDR) and Word Error Rate (WER), obtained during realistic double-talk situations. Further, we compare the postﬁlter against a state-of-the art implementation. Finally, we analyze the numerical complexity of the overall system.
Stochastic Curiosity Exploration for Dialogue Systems

Traditionally, task-oriented dialogue system is built by an autonomous agent which can be trained by reinforcement learning where the reward from environment is maximized. The agent is learned by updating the policy when the goal state is observed. However, in real world, the extrinsic reward is usually sparse or missing. The training efﬁciency is bounded. The system performance is degraded. It is challenging to tackle the issue of sample efﬁciency in sparse reward scenario for spoken dialogues. Accordingly, a dialogue agent needs additional information to update its policy even in the period when reward is absent in the environment. This paper presents a new dialogue agent which is learned by incorporating the intrinsic reward based on the information-theoretic approach via stochastic curiosity exploration. This agent encourages the exploration for future diversity based on a latent dynamic architecture which consists of encoder network, curiosity network, information network and policy network. The latent states and actions are drawn to predict stochastic transition for future. The curiosity learning are implemented with intrinsic reward in a metric of mutual information and prediction error in the predicted states and actions. Experiments on dialogue management using PyDial demonstrate the beneﬁt by using the stochastic curiosity exploration.
Computer Audition for Continuous Rainforest Occupancy Monitoring: The Case of Bornean Gibbons' Call Detection

Auditory data is used by ecologists for a variety of purposes, including identifying species ranges, estimating population sizes, and studying behaviour. Autonomous recording units (ARUs) enable auditory data collection over a wider area, and can provide improved consistency over traditional sampling methods. The result is an abundance of audio data – much more than can be analysed by scientists with the appropriate taxonomic skills. In this paper, we address the divide between academic machine learning research on animal vocalisation classiﬁers, and their application to conservation efforts. As a unique case study, we build a Bornean gib-bon call detection system by ﬁrst manually annotating existing data, and then comparing audio analysis tool kits including end-to-end and bag-of-audio-word modelling. Finally, we propose a deep architecture that outperforms the other approaches with respect to unweighted average recall. The code is available at: https://github.com/glam-imperial/ Bornean-Gibbons-Call-Detection
Small traits with big consequences: how seed traits of nitrogen‐fixing plants might influence ecosystem nutrient cycling

Symbiotic nitrogen (N)‐fixing plants have important effects on the biogeochemical processes of the sites they inhabit, but their ability to reach these sites is determined by the dispersal of their seeds. Differences in seed size and dispersal vectors of N‐fixing and non‐fixing plants could influence the spatial and temporal distributions of N fixers, and thus could have important impacts on biogeochemical cycling. Using seed mass, dispersal vector, and biome data retrieved from online public databases, we ask if there are systematic differences in seed mass and dispersal vectors between N‐fixing and non‐fixing plants. We demonstrate that rhizobial N fixers tend to have larger seeds that are more likely to be biotically dispersed than seeds of non‐fixers, whereas actinorhizal N‐fixing trees tend to have small, abiotically dispersed seeds. We then synthesize existing evidence from the literature to draw links between these dispersal traits and the spatio–temporal patterns of N fixers, as well as their biogeochemical effects on terrestrial ecosystems. Using this literature, we argue that the spatio–temporal distributions of N fixers are influenced by their seed dispersal characteristics, and that these distribution patterns have important effects on the total amount of N fixed at a site and the timing of N inputs during processes such as succession.
Spoken Content and Voice Factorization for Few-Shot Speaker Adaptation

The low similarity and naturalness of synthesized speech remain a challenging problem for speaker adaptation with few resources. Since the acoustic model is too complex to interpret, overﬁtting will occur when training with few data. To prevent the model from overﬁtting, this paper proposes a novel speaker adaptation framework that decomposes the parameter space of the end-to-end acoustic model into two parts, with the one on predicting spoken content and the other on modeling speaker’s voice. The spoken content is represented by phone posterior-gram (PPG) which is speaker independent. By adapting the two sub-modules separately, the overﬁtting can be alleviated effectively. Moreover, we propose two different adaptation strategies based on whether the data has text annotation. In this way, speaker adaptation can also be performed without text annotations. Experimental results conﬁrm the adaptability of our proposed method of factorizating spoken content and voice. Lis-tening tests demonstrate that our proposed method can achieve better performance with just 10 sentences than speaker adaptation conducted on Tacotron in terms of naturalness and speaker similarity.