The task of quantifying human behavior by observing interaction cues is an important and useful one across a range of domains in psychological research and practice. Machine learning-based approaches typically perform this task by first estimating behavior based on cues within an observation window, such as a fixed number of words, and then aggregating the behavior over all the windows in that interaction

We propose a data-to-document generator that can easily control the contents of output texts based on a neural language model. Conventional data-to-text model is useful when a reader seeks a global summary of data because it has only to describe an important part that has been extracted beforehand. However, since it differs from users to users what they are interested in, it is necessary to develop

We describe a novel way to implement subword language models in speech recognition systems based on weighted finite state transducers, hidden Markov models, and deep neural networks. The acoustic models are built on graphemes in a way that no pronunciation dictionaries are needed, and they can be used together with any type of subword language model, including character models. The advantages of short

Replay attacks pose the most severe threat to automatic speaker verification systems among various spoofing attacks. In this paper, we propose a novel feature extraction method that leverages both the phase-based and magnitude-based features. The proposed method fully utilizes the subband information and the complementary information from the phase and magnitude spectra. First, we conduct a discriminative

Acoustic and articulatory differences between spoken and shouted vowels were analyzed for two male and two female subjects by means of acoustic recordings and midsagittal magnetic resonance images of the vocal tract. In accordance with previous acoustic findings, the fundamental frequencies, intensities, and formant frequencies were all generally higher for shouted than for spoken vowels. The harmonics-to-noise

End-to-end spoken language translation (SLT) has recently gained popularity thanks to the advancement of sequence to sequence learning in its two parent tasks: automatic speech recognition (ASR) and machine translation (MT). However, research in the field has to confront with the scarcity of publicly available corpora to train data-hungry neural networks. Indeed, while traditional cascade solutions

The real-time Magnetic Resonance Imaging (rtMRI) video captures the vocal tract movements in the mid-sagittal plane during speech. Air tissue boundaries (ATBs) are contours that trace the transition between the high-intensity tissue corresponding to the speech articulators and the low-intensity airway cavity in the rtMRI video. The ATB segmentation in an rtMRI video is a common preprocessing step which

We present a novel abstractive document summarization based on the recently proposed dynamic convolutional encoder-decoder architectures. We address several aspects of summarization that are not well modeled by the basic architecture, by integrating multiple layers of the encoder, controlling information flow in the hierarchy, and exploiting external knowledge. First, we propose a simple and efficient

This paper presents novel neural network based language models that can correct automatic speech recognition (ASR) errors by using speech recognizer outputs as a context. Our proposed models, called neural candidate-aware language models (NCALMs), estimate the generative probability of a target sentence while considering ASR outputs including hypotheses and their posterior probabilities. Recently,

Web search users are likely to face problems related to the availability of large amounts of data. As the quantity of online content grows, the risk of missing relevant information during search can only increase. Moreover, external variables such as the users’ reading proficiency level can further complicate the task. This article proposes an online multi-document summarization algorithm for text

We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach

Designing a Spoken Language Understanding (SLU) system for command-and-control applications is challenging. Both Automatic Speech Recognition and Natural Language Understanding are language and application dependent to a great extent. Even with a lot of design effort, users often still have to know what to say to the system for it to do what they want. We propose to use an end-to-end SLU system that

The vulnerability of Automatic Speaker Verification (ASV) systems to spoofing or presentation attacks is still an open security issue. In this context, replay spoofing attacks pose a great threat to an ASV system since they can be easily performed (using a playback device, and without needing any technical skill). In this paper, we analyze replay speech signals in terms of reverberation that may occur

The sequence-to-sequence (Seq2Seq) model generates target words iteratively given the previously observed words during the decoding process, which results in the loss of the holistic semantics in the target response and the complete semantic relationship between responses and dialogue histories. In this paper, we propose a generic diversity-promoting joint network, called Holistic Semantic Constraint

Social robotics is becoming a reality and voice-based human-robot interaction is essential for a successful human-robot collaborative symbiosis. The main objective of this paper is to assess the effect of visual servoing in the performance of a linear microphone array regarding distant ASR in a mobile, dynamic and non-stationary robotic testbed that can be representative of real HRI scenarios. Visual

This paper focuses on estimating the quality of a clustering process. The task is to cluster short speech segments that belong to different speakers. A variety of statistical parameters are estimated from the output of the clustering process. These parameters are used to train a logistic regression to serve as a clustering quality estimation system. In this paper, mean-shift clustering with either

In this paper, a new feature set is proposed for use in a playback attack detector (PAD) aimed at safeguarding a passphrase and speaker-verified protected system that can be remotely accessed from an arbitrary location using an arbitrary telecommunication channel. The new feature set, termed VoicedTracks, is a time-frequency map of the most robust harmonic trajectories in an utterance and serves as

Over the past few years, Twitter has experienced massive growth and the volume of its online content has increased rapidly. This content has been a rich source for several studies that focused on natural language processing (NLP) research. However, Twitter data pose numerous challenges and obstacles to NLP tasks. For the English language, Twitter has an NLP tool that provides tweet-specific NLP tasks

This paper describes a new method for speech activity detection (SAD) based on the recently proposed single frequency filtering (SFF) analysis of speech signals and a neural network model. The SFF analysis gives instantaneous spectrum of the speech signal at each sampling instant. The frequency resolution of the spectrum is decided by the number of frequencies used in the SFF analysis, which in turn

Named entity recognition (NER) is a fundamental task in natural language processing. The existing Korean NER methods use the Korean morpheme, syllable sequence, and part-of-speech as features, and use a sequence labeling model to tackle this problem. In Korean, on one hand, morpheme itself contains strong indicative information of named entity (especially for time and person). On the other hand, the

The evolution of modern voice-controlled devices (VCDs) has revolutionized the Internet of Things (IoT) and resulted in the increased realization of smart homes, personalization, and home automation through voice commands. These VCDs can be exploited in IoT driven environments to generate various spoofing attacks, including the chaining of replay attacks (i.e. multi-order replay attacks). Existing

Phonological disorders are characterized by substitutions, insertion and/or deletions of sounds during the process of language acquisition, which are known as Phonological Processes (PPs). In the speech therapy domain, an early identification of PPs allows the diagnosis and treatment of various pathologies and may improve clinical tasks, however, there are few proposals that focus on the identification

Almost 50 million people are living with dementia in 2018 worldwide, and the number will double every 20 years. The effectiveness of existing pharmacologic treatments for the disease is limited to symptoms control, and none of them are able to prevent, reverse or turn off the neurodegenerative process that leads to dementia; therefore, a prompt detection of the “disease signature” is a key problem

Keyphrase extraction is the task of extracting the most important phrases from a document. Automatic keyphrase extraction attempts to itemize a document content as metainformation and facilitate efficient information retrieval. In this paper we propose TOP-Rank, an approach for keyphrase extraction and keyphrase classification. For keyphrase extraction, we build an approach based on the position of

Natural Language Generation (NLG) plays a critical role in Spoken Dialogue Systems (SDSs), aims at converting a meaning representation into natural language utterances. Recent deep learning-based generators have shown improving results irrespective of providing sufficient annotated data. Nevertheless, how to build a generator that can effectively utilize as much of knowledge from a low-resource setting

Research in the area of automatic monitoring of emotional state from speech permits envisaging future novel applications for the remote monitoring of some common mental disorders, such as depression. However, these tools raise some privacy concerns since speech is sent via telephone or the Internet, and it is further stored or processed in remote servers. Speaker de-identification can be used to protect

In clinical practice, assessment of intelligibility in speakers with dysarthria is performed by speech-language pathologists through auditory perceptual tests which demand patients’ presence at hospital and involve time-consuming examinations. Frequent clinical monitoring can be costly and logistically inconvenient both for patients and medical experts. Here, we aim to automate the procedure of assessment

Research in automatic affect recognition has seldom addressed the issue of computational resource utilization. With the advent of ambient intelligence technology which employs a variety of low-power, resource-constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions may rely on monitoring

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as “presentation attacks.” These vulnerabilities are generally unacceptable and call for spoofing countermeasures or “presentation attack detection” systems. In addition to impersonation

Automatic Speaker Verification (ASV) systems have been widely applied for speaker authentication for biometric security especially in e-business scenarios. However, vulnerabilities of the ASV technology have been discovered and have generated much interest to design anti-spoofing countermeasures. Serious threats can be posed by replay attacks, which are difficult to detect and easy to mount with accessible

Automatic Speaker Verification (ASV) is authentication of individuals by analyzing their speech signals. Different synthetic approaches allow spoofing to deceive ASV systems (ASVs), whether using techniques to imitate a voice or reconstruct the features. Attackers beat up the ASVs using four general techniques; impersonation, speech synthesis, voice conversion, and replay. The last technique is considered

Code-switching refers to the frequent use of non-native language words/phrases by speakers while conversating in their native languages. Traditionally, for training a language model (LM) for code-switching data, one is required to tediously collect a large amount of text corpus in the respective code-switching domain. Alternately, we recently proposed a more viable approach that adapts an existing

With the recent advancements in Artificial Intelligence(AI), Intelligent Virtual Assistants (IVA) such as Alexa and Google Home have become a ubiquitous part of every home. Currently, such IVAs are mostly audio-based, but going forward, we are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances. This will

In order to properly train an automatic speech recognition system, speech with its annotated transcriptions is most often required. The amount of real annotated data recorded in noisy and reverberant conditions is extremely limited, especially compared to the amount of data than can be simulated by adding noise to clean annotated speech. Thus, using both real and simulated data is important in order

The use of background knowledge is largely unexploited in text classification tasks. This paper explores word taxonomies as means for constructing new semantic features, which may improve the performance and robustness of the learned classifiers. We propose tax2vec, a parallel algorithm for constructing taxonomy-based features, and demonstrate its use on six short text classification problems: prediction

A new approach for determining the glottal activity from speech signals is presented in this paper. The approach is based on the use of single frequency filtering (SFF), proposed recently for voice activity detection. The variance (across frequency) of the spectral envelopes at each sampling instant is derived using the SFF of speech signal. The variance plot shows discontinuities corresponding to

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should capture phonetic content and abstract away from other types of variability, such as speaker differences and channel noise. Previous work in this

Automatic speech recognition for child speech has been long considered a more challenging problem than for adult speech. Various contributing factors have been identified such as larger acoustic speech variability including mispronunciations due to continuing biological changes in growth, developing vocabulary and linguistic skills, and scarcity of training corpora. A further challenge arises when

Emotional speech production has been previously studied using fleshpoint tracking data in speaker-specific experiment setups. The present study introduces a real-time magnetic resonance imaging database of emotional speech production from 10 speakers and presents articulatory analysis results of speech emotional expression using the database. Midsagittal vocal tract parameters (midsagittal distances

We present an end-to-end approach for knowledge-grounded response generation in Dialog System Technology Challenges 7 (DSTC7). Our system is trained by a pointer generator model, so that an output token in a response can either be generated or copied from conversation history or facts according to a trainable action probability distribution. Furthermore, to minimize generating meaningless responses

Audio-Visual Scene-Aware Dialog (AVSD) is best understood as an extension of Visual Question Answering, the task of generating a textual answer in response to a textual question on multi-media content. In AVSD, the answer-relevant “context” is expanded to include past dialog turns, which we view as a specialized form of extra textual knowledge (in addition to the standard video features). We have developed

Children speech recognition is challenging mainly due to the inherent high variability in children's physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children's life. Part of the challenge is due to the lack of large amounts of available children speech data for efficient

Computer-Assisted Pronunciation Training (CAPT) systems aim to help a child learn the correct pronunciations of words. However, while there are many online commercial CAPT apps, there is no consensus among Speech Language Therapists (SLPs) or non-professionals about which CAPT systems, if any, work well. The prevailing assumption is that practicing with such programs is less reliable and thus does

Methods from automatic speech recognition (ASR), such as segmentation and forced alignment, have facilitated the rapid annotation and analysis of very large adult speech databases and databases of caregiver-infant interaction, enabling advances in speech science that were unimaginable just a few decades ago. This paper centers on two main problems that must be addressed in order to have analogous resources

Child engagement is defined as the interaction of a child with his/her environment in a contextually appropriate manner. Engagement behavior in children is linked to socio-emotional and cognitive state assessment with enhanced engagement identified with improved skills. A vast majority of studies however rely solely, and often implicitly, on subjective perceptual measures of engagement. Access to automatic

Non-verbal communication involves encoding, transmission and decoding of non-lexical cues and is realized using vocal (e.g. prosody) or visual (e.g. gaze, body language) channels during conversation. These cues perform the function of maintaining conversational flow, expressing emotions, and marking personality and interpersonal attitude. In particular, non-verbal cues in speech such as paralanguage

We propose a practical, feature-level and score-level fusion approach by combining acoustic and estimated articulatory information for both text independent and text dependent speaker verification. From a practical point of view, we study how to improve speaker verification performance by combining dynamic articulatory information with the conventional acoustic features. On text independent speaker

How the speech production and perception systems evolved in humans still remains a mystery today. Previous research suggests that human auditory systems are able, and have possibly evolved, to preserve maximal information about the speaker's articulatory gestures. This paper attempts an initial step towards answering the complementary question of whether speakers' articulatory mechanisms have also

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to

Pathological speech usually refers to the condition of speech distortion resulting from atypicalities in voice and/or in the articulatory mechanisms owing to disease, illness or other physical or biological insult to the production system. Although automatic evaluation of speech intelligibility and quality could come in handy in these scenarios to assist experts in diagnosis and treatment design, the

In this article we investigate what representations of acoustics and word usage are most suitable for predicting dimensions of affect|AROUSAL, VALANCE, POWER and EXPECTANCY|in spontaneous interactions. Our experiments are based on the AVEC 2012 challenge dataset. For lexical representations, we compare corpus-independent features based on psychological word norms of emotional dimensions, as well as

For several decades now, there has been sporadic interest in automatically characterizing the speech impairment due to Parkinson's disease (PD). Most early studies were confined to quantifying a few speech features that were easy to compute. More recent studies have adopted a machine learning approach where a large number of potential features are extracted and the models are learned automatically

Laryngeal high-speed videoendoscopy is a state-of-the-art technique to examine physiological vibrational patterns of the vocal folds. With sampling rates of thousands of frames per second, high-speed videoendoscopy produces a large amount of data that is difficult to analyze subjectively. In order to visualize high-speed video in a straightforward and intuitive way, many methods have been proposed

Segmental and suprasegmental speech signal modulations offer information about paralinguistic content such as affect, age and gender, pathology, and speaker state. Speaker state encompasses medium-term, temporary physiological phenomena influenced by internal or external biochemical actions (e.g., sleepiness, alcohol intoxication). Perceptual and computational research indicates that detecting speaker

Individuals with severe motor impairments commonly enter text using a single binary switch and symbol scanning methods. We present a new scanning method -Huffman scanning - which uses Huffman coding to select the symbols to highlight during scanning, thus minimizing the expected bits per symbol. With our method, the user can select the intended symbol even after switch activation errors. We describe

Language is being increasingly harnessed to not only create natural human-machine interfaces but also to infer social behaviors and interactions. In the same vein, we investigate a novel spoken language task, of inferring social relationships in two-party conversations: whether the two parties are related as family, strangers or are involved in business transactions. For our study, we created a corpus

Artificial talkers and speech synthesis systems have long been used as a means of understanding both speech production and speech perception. The development of an airway modulation model is described that simulates the time-varying changes of the glottis and vocal tract, as well as acoustic wave propagation, during speech production. The result is a type of artificial talker that can be used to study

A sentence verification task (SVT) was used to study the effects of sentence predictability on comprehension of natural speech and synthetic speech that was controlled for intelligibility. Sentences generated using synthetic speech were matched on intelligibility with natural speech using results obtained from a separate sentence transcription task. In the main experiment, the sentence verification