This study proposes a language and domain independent approach for automatic extractive text summarization (EATS) tasks, which is based on a clustering scheme supported by a genetic algorithm (GA), to find an optimal grouping of sentences. Furthermore, our approach includes a topic modeling algorithm to find the key sentences in clusters based on automatically generated keywords. Our experimental results

In 2019, media reports raised awareness about privacy and security violations in Conversational User Interfaces (CUI) like Alexa, Siri and Google. Users report that they perceive CUI as creepy and that they are concerned about their privacy. The European General Data Protection Regulation (GDPR) and several other laws across the globe, give users the right to control processing of their data, for example

This paper focuses on the task of cross-language relation extraction, which aims to identify the semantic relations holding between entities in the text. The goal of the task is to train classifiers for low-resource languages by means of the annotated data from high-resource languages. Related methods usually employ parallel data or Machine Translator (MT) to project annotated data from a source to

Sentence embedding is an influential research topic in natural language processing (NLP). Generation of sentence vectors that reflect the intrinsic meaning of sentences is crucial for improving performance in various NLP tasks. Therefore, numerous supervised and unsupervised sentence-representation approaches have been proposed since the advent of the distributed representation of words. These approaches

When interviewing a child who may have witnessed a crime, the interviewer must ask carefully directed questions in order to elicit a truthful statement from the child. The presented work uses Granger causal analysis to examine and represent child–interviewer interaction dynamics over such an interview. Our work demonstrates that Granger Causal analysis of psycholinguistic and acoustic signals from

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, vocabulary coverage issues, differences in construction method and discrepancies in emotion framework and representation result in a heterogeneous landscape of emotion detection resources, calling for a unified approach to utilizing them. To combat this, we present an extended emotion lexicon

Automatic sign language translation process relies mainly on dictionaries of signs to interpret the right meaning of gestures. Due to the lack of large multi sign language dictionaries covering all the aspect of sign languages, the collaborative approach to create signs becomes essential. In fact, the collaborative sign creation process based on Kinect motion capture tool requires the collaboration

Most automatic speech recognition (ASR) systems are optimised for one specific language and their performance consequently deteriorates drastically when confronted with multilingual or code-switched speech. We describe our efforts to improve an ASR system that can process code-switched South African speech that contains English and four indigenous languages: isiZulu, isiXhosa, Sesotho and Setswana

The recently proposed VBx diarization method uses a Bayesian hidden Markov model to find speaker clusters in a sequence of x-vectors. In this work we perform an extensive comparison of performance of the VBx diarization with other approaches in the literature and we show that VBx achieves superior performance on three of the most popular datasets for evaluating diarization: CALLHOME, AMI and DIHARD

In this paper, a novel approach to unsupervised pattern discovery for speech signals is proposed. Recently, we introduced an image processing method (IPM) that extracts the desired keywords present in a pair of speech utterances. This method performs well in detecting true positives but, at the same time it also detects higher number of false positives. Therefore, this paper aims to reduce the detection

In this paper, we address the problem of speaker verification in conditions unseen or unknown during development. A standard method for speaker verification consists of extracting speaker embeddings with a deep neural network and processing them through a backend composed of probabilistic linear discriminant analysis (PLDA) and global logistic regression score calibration. This method is known to result

This paper investigates the ability of multilingual BERT (mBERT) language model to transfer syntactic knowledge cross-lingually, verifying if and to which extent syntactic dependency relationships learnt in a language are maintained in other languages. In detail, the main contributions of this paper are: (i) an analysis of the cross-lingual syntactic transfer capability of mBERT model; (ii) a detailed

Text-to-Speech (TTS) synthesis plays an important role in human-computer interaction. Currently, most TTS technologies focus on the naturalness of speech, namely, making the speeches sound like humans. However, the key tasks of the expression of emotion and the speaker identity are ignored, which limits the application scenarios of TTS synthesis technology. To make the synthesized speech more realistic

Authorship attribution is the process of identifying the author of a text. Approaches to tackling it have been conventionally divided into classification-based ones, which work well for small numbers of candidate authors, and similarity-based methods, which are applicable for larger numbers of authors or for authors beyond the training set; these existing similarity-based methods have only embodied

In this paper, we propose a frequency-domain adaptive line enhancer (ALE) to reduce nonstationary harmonic noise, such as medical equipment beeps, from a noisy speech signal captured by a single microphone. The reduction of nonstationary noise is very challenging, with the tradeoff between noise reduction and speech distortion, often resulting with much noise residuals. The proposed ALE is a combination

Generative Adversarial Nets (GAN) has been successfully introduced to unconditional generating text to alleviate exposure bias. However, the discriminator in this model only evaluates the entire sequence, which causes feedback sparsity and mode collapse. To tackle these problems, we propose a novel mechanism. The mechanism first segments the entire sequence into several subsequences. Then, these subsequences

Embeddings are very popular representations that allow computing semantic and syntactic similarities between linguistic units from text co-occurrence matrix. Units can vary from character n-grams to words, including more coarse-grained units such as sentences and documents. Recently, multi-level embeddings combining representations from different units have been proposed as an alternative to single-level

The writing system of Central Kurdish features three cases in which there is no one-to-one mapping between the orthographical letters and the phonemes of the language. Consequently, the written words including these cases may be pronounced in multiple ways. The process of finding the correct pronunciation of written words is called Grapheme-to-Phoneme (G2P) conversion and is a key step in natural language

This paper argues that training Generative Adversarial Networks (GANs) on local and non-local dependencies in speech data offers insights into how deep neural networks discretize continuous data and how symbolic-like rule-based morphophonological processes emerge in a deep convolutional architecture. Acquisition of speech has recently been modeled as a dependency between latent space and data generated

Syllabication algorithms have been developed for many languages because of the important role that syllables play in language processing. However, efforts continue to find better methods. In this study, we take a second look at the performance of two major approaches to syllabication to help determine their relationship to the structure of languages and their orthographies. We applied syllabification

We investigate the predictive capability of mathematical models of the type-token relationship applied to the vocabulary growth profiles of selected English language documents. We compare the existing Good-Toulmin and Heaps formulae with an alternative approach based on Bernoulli trial word selection from a fixed finite vocabulary using the Zipf and Zipf-Mandelbrot probability distributions. We make

Speech encodes a wealth of information related to human behavior and has been used in a variety of automated behavior recognition tasks. However, extracting behavioral information from speech remains challenging including due to inadequate training data resources stemming from the often low occurrence frequencies of specific behavioral patterns. Moreover, supervised behavioral modeling typically relies

Glottal source characteristics vary between phonation types due to the tension of laryngeal muscles with the respiratory effort. Previous studies in the classification of phonation type have mainly used speech signals recorded by microphone. Recently, two studies were published in the classification of phonation type using neck surface accelerometer (NSA) signals. However, there are no previous studies

The vowel transition regions are the crucial landmarks in the speech signal. These vital regions are present at both ends of the vowel. They lie in the junction between a consonant and a vowel (CV) regions. This region plays an important role in numerous speech applications like speaker recognition, emotion conversion, speech rate modification, and CV unit recognition. The performance of these applications

In this article, we propose a new method for joint cochannel speaker separation and recognition called adaptive-dictionary non-negative matrix deconvolution (DANMD). This method is an extension of non-negative matrix deconvolution (NMD) which models spectrogram matrix as a linear combination of dictionary elements (atoms). We propose a dictionary which is a linear combination of speaker-independent

In this paper, we propose a novel method to segment and label pass-phrase utterances for training deep neural network (DNN) bottleneck (BN) features for text-dependent speaker verification (TD-SV). Specifically, gender-dependent hidden Markov models (HMMs) for monophones are first trained using the pass-phrase utterances that are disjoint from evaluation. Next, the trained HMMs are speaker-adapted

The present work proposes a text-independent dialect identification system. Generally, dialects of a language exhibit varying pronunciation styles followed in a specific geographical region. In this paper, chroma features familiar with music-related systems are employed for identification of dialects. In addition, eight significant spectral shape related features from short term spectra are computed

Capsule networks (CapsNets) have recently gotten attention as a novel neural architecture. This paper presents the sequential routing framework which we believe is the first method to adapt a CapsNet-only structure to sequence-to-sequence recognition. Input sequences are capsulized then sliced by a window size. Each slice is classified to a label at the corresponding time through iterative routing

Aspect-based sentiment analysis is a special type of sentiment analysis that aims to identify the discussed aspects and their sentiment polarities in a given review. In this paper, two deep learning models are proposed to address essential aspect-based sentiment analysis tasks: aspect-category identification and aspect-sentiment classification. For the first task, an identification model is proposed

In voice production the random variation of the glottal cycles in relation to a mean value caused by the (quasi-) periodic movement of the vocal folds generates the jitter phenomenon. Its study is important due to applications as identification of pathologies related to the voice, improvement of the naturalness of synthesized voices and calibration of signal processing algorithms to identify glottal

Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired language disorder most commonly caused by stroke. Assessment of speech performance using picture naming tasks is a key method for both diagnosis and monitoring of responses to treatment interventions by people with aphasia (PWA). Currently, this assessment is conducted manually by speech and language therapists (SLT). Surprisingly

Nasalization of vowels is a phenomenon where the oral and nasal tracts participate simultaneously in the production of speech. Acoustic coupling of the oral and nasal tracts, together with the glottal activity, results in a complex dynamic production system. The extent of coupling determines the degree of nasalization in vowels. Changes in the instantaneous response of the vocal tract system are used

Mild Cognitive Impairment (MCI) is a heterogeneous clinical syndrome, often considered as the prodromal stage of dementia. It is characterized by the subtle deterioration of cognitive functions, including memory, executive functions and language. Mainly due to the tenuous nature of these impairments, a high percentage of MCI cases remain undetected. There is evidence that language changes in MCI are

Dysarthria is a condition which hampers the ability of an individual to control the muscles that play a major role in speech delivery. The loss of fine control over muscles that assist the movement of lips, vocal chords, tongue and diaphragm results in abnormal speech delivery. One can assess the severity level of dysarthria by analyzing the intelligibility of speech spoken by an individual. Continuous

While human listeners can, to some extent, understand the information conveyed by the speech signal when it is mixed with noise, traditional objective intelligibility measures usually fail to operate without a priori knowledge of the clean speech signal. This hence limits the usability of those measures in situations where the clean speech signal is inaccessible. In this paper a glimpse-based method

Recently, a variety of model designs and methods have blossomed in the context of the sentiment analysis domain. However, there is still a lack of comprehensive studies of Aspect-based Sentiment Analysis. We want to fill this gap and propose a comparison with ablation analysis of Aspect Term Extraction using various text embeddings methods. We particularly focused on simple architectures based on long

Heart failure (HF) is a major global health concern and is increasing in prevalence. It affects the larynx and breathing – thereby the quality of speech. In this article, we propose an approach for the automatic detection of people with HF using the speech signal. The proposed method explores mel-frequency cepstral coefficient (MFCC) features, glottal features, and their combination to distinguish

The pharyngeal fricative is a typical compensatory articulation disorder in cleft palate speech. It is produced by retracting the root of the tongue to the posterior pharyngeal wall to substitute for the fricatives and affricates produced in the oral cavity. People who use the pharyngeal fricative have difficulties in daily communication. Research on automatic pharyngeal fricative detection can provide

Representation transfer learning has been widely used across a range of machine learning tasks. One such notable approach seen in the speech literature is the use of Convolutional Neural Networks, pre-trained for image classification tasks, to extract features from spectrograms of speech signals. Interestingly, despite the strong performance of such approaches, there have been minimal research efforts

Far-field speech processing is an important and challenging problem. In this paper, we propose deep ad-hoc beamforming, a deep-learning-based multichannel speech enhancement framework based on ad-hoc microphone arrays, to address the problem. It contains three novel components. First, it combines ad-hoc microphone arrays with deep-learning-based multichannel speech enhancement, which reduces the probability

Robust speaker recognition, including in the presence of malicious attacks, is becoming increasingly important and essential, especially due to the proliferation of smart speakers and personal agents that interact with an individual’s voice commands to perform diverse and even sensitive tasks. Adversarial attack is a recently revived domain which is shown to be effective in breaking deep neural network-based

Prosody is the supra-segmental aspect of speech that helps to convey the structure and intended meaning of lexical content unambiguously. The automatic detection of prosodic events, such as phrase boundary and word prominence, has a number of applications in discourse analysis, where a combination of syntactic and acoustic-prosodic features is typically employed. This work addresses prosodic event

This paper presents a joint syntactic-semantic embedding model which not only uses syntactic information to enrich the word embeddings but also generates distributed representations for the syntactic structures themselves. The syntactic input to our model comes from a Lexicalized Tree-Adjoining Grammar parser. The word embeddings from our model outperform the Skip-gram embeddings in several word similarity

Alzheimer’s disease (AD) is one of the most common forms of dementia in the world. The Mini-Mental State Examination, a tool developed to detect AD, is composed of various tests that evaluate functional performance in several fields, one of which is language. Several symptoms are manifested in voices as a result of language and speech problems caused by AD, including frequent involuntary pauses during

Global vectors, or global embeddings, are important word representations for many natural language processing tasks. With the popularity of dynamic embeddings (also known as contextual embeddings, such as ELMo and BERT) in recent years, attentions on global vectors have been diverted to a large extent. While, compared to the dynamic embeddings, the global embeddings are faster to train, straightforward

Short text is the main carrier for people to express their ideas and opinions. And it is very important as well a big challenge to understand the meaning of short text or recognize the semantic patterns of different short texts. Most of existing methods use word embedding and short text interaction to learn short text pairs semantic patterns. However, some of these methods are complicated and cannot

This paper presents a textual entailment (TE) model that considers entailment as an optimization problem. The proposed TE model employs a genetic algorithm to derive an optimal similarity function and correlated entailment judgment threshold. The similarity function is formulated through a linear combination of text similarity measures and weights. Two text similarity measures are considered: cosine

Verbal fluency – i.e. the ability to name as many words of a given category as possible in a defined time interval – is an integral part of neuropsychological test batteries for the diagnosis of dementia. Verbal fluency can be easily administered and thus may also be implemented in computerized dementia screening tests. In the present study we sought to investigate the capability of phonemic verbal

Neural networks have been widely used in the field of text classification, and have achieved good results on various Chinese datasets. However, for long text classification, there are a lot of redundant information in text data, and some of the redundant information may involve other topic information, which makes long text classification challenging. To solve the above problems, this paper proposes

Neural Referring Expression Generation (REG) models have shown promising results in generating expressions which uniquely describe visual objects. However, current REG models still lack the ability to produce diverse and unambiguous referring expressions (REs). To address the lack of diversity, we propose generating a set of diverse REs, rather than one-shot REs. To reduce the ambiguity of referring

The accelerating flow of information we encounter around the world today makes many companies deploy speech recognition systems that, to an ever-growing extent, process data on-line rather than off-line. These systems, e.g., for real-time 24/7 broadcast transcription, often work with input-stream data containing utterances in more than one language. This multilingual data can correctly be transcribed

Early prediction of neurodegenerative disorders such as Alzheimer’s disease (AD) and related dementias may facilitate earlier access to medical and social supports. Further, detection of individuals with preclinical disease may help to enrich clinical trial populations for studies examining disease-modifying interventions. Changes in speech and language patterns may occur in the early stages of neurodegenerative

Intuitively, image- or video-based recommendations seem to be more reliable than those containing plain text, and these types of recommendations have recently become widely encouraged and commonly seen across opinion sharing platforms. Considering their potential for manipulation, graphs (e.g., images and videos) are more vulnerable to spam than scripts. However, most state-of-the-art solutions for

Text generation with auxiliary attributes, such as topics or sentiments, has made remarkable progress. However, high-quality labeled data is difficult to obtain for the large-scale corpus. Therefore, this paper focuses on social emotion ranking aiming to identify social emotions with different intensities evoked by online documents, which could be potentially beneficial for further controlled text

Neural sequence-to-sequence text-to-speech synthesis (TTS) can produce high-quality speech directly from text or simple linguistic features such as phonemes. Unlike traditional pipeline TTS, the neural sequence-to-sequence TTS does not require manually annotated and complicated linguistic features such as part-of-speech tags and syntactic structures for system training. However, it must be carefully

The taking of turns is a fundamental aspect of dialogue. Since it is difficult to speak and listen at the same time, the participants need to coordinate who is currently speaking and when the next person can start to speak. Humans are very good at this coordination, and typically achieve fluent turn-taking with very small gaps and little overlap. Conversational systems (including voice assistants and

Headline generation is a task where the most important information of a news article is condensed and embodied into a single short sentence. This task is normally addressed by summarization techniques, ideally combining extractive and abstractive methods together with sentence compression or fusion techniques. Although Natural Language Generation (NLG) techniques have not been directly exploited for