Speaker recognition continues to grow as a research challenge in the field with expanded application in commercial, forensic, educational and general speech technology interfaces. However, challenges remain, especially for naturalistic audio streams including recordings with mismatch between train and test data (i.e., when train or system development data and enrollment/test data or application data

The desired speech detector plays an important role for controlling the speech distortion in spatial filtering based speech enhancement algorithms. However, the conventional complex coherence(CC) based algorithms can only distinguish the coherent speech and diffuse noise. To improve the performance on the scenarios that both the coherent interference and diffuse noise are present, we propose a directional

In the paper, we investigate a graph representation of speech signals and graph speech enhancement technology. Specifically, we first propose a new graph k-shift operator Ck to map speech signals into the graph domain and construct a novel graph Fourier basis by using its singular eigenvectors for speech graph signals (SGSs). On this basis, we propose an improved graph Wiener filtering method based

Recently, spontaneous speech emotion recognition has become an active and challenging research subject. This paper proposes a new method of spontaneous speech emotion recognition by using deep multimodal audio feature learning based on multiple deep convolutional neural networks (multi-CNNs). The proposed method initially generates three different audio inputs for multi-CNNs so as to learn deep multimodal

Speech perception and spoken word recognition are not only affected by what is being said, but also by who is speaking. Currently, publicly available corpora of spoken Dutch do not offer a wide variety of linguistic materials produced by multiple talkers. The VariaNTS (Variatie in Nederlandse Taal en Sprekers) corpus is a Dutch spoken corpus that was developed to maximize both linguistic and talker

The present study investigates whether native speakers of German phonetically accommodate to natural and synthetic voices in a shadowing experiment. We aim to determine whether this phenomenon, which is frequently found in HHI, also occurs in HCI involving synthetic speech. The examined features pertain to different phonetic domains: allophonic variation, schwa epenthesis, realization of pitch accents

The performance of voice-based Parkinson’s disease (PD) detection systems degrades when there is an acoustic mismatch between training and operating conditions caused mainly by degradation in test signals. In this paper, we address this mismatch by considering three types of degradation commonly encountered in remote voice analysis, namely background noise, reverberation and nonlinear distortion, and

In this paper, a multilingual automatic speech recognition (ASR) and language identification (LID) system is designed. In contrast to conventional multilingual ASR systems, this paper takes advantage of the complementarity of the ASR and LID modules. First, the LID module contributes to the language adaptive training of the multilingual acoustic model. Then, the ASR decoding information acts as the

Burushaski is an endangered language isolate spoken in Hunza, Nager, and Yasin valleys of Gilgit, Northern Pakistan. The present study investigates the acoustic correlates of Hunza Burushaski’s three-way stop laryngeal contrast (voiceless unaspirated, voiceless aspirated, and voiced unaspirated) across five places of articulation (bilabial, dental, retroflex, palatal, and velar). A wide range of acoustic

There is ample evidence that both native and non-native listeners deal with speech variation by quickly tuning into a speaker and adjusting their phonetic categories according to the speaker’s ambiguous pronunciation. This process is called lexically-guided perceptual learning. Moreover, the presence of noise in the speech signal has previously been shown to change the word competition process by increasing

Communicating emotion is essential in building and maintaining relationships. We communicate our emotional state not just with the words we use, but also how we say them. Changes in the rate of speech, short-term energy and intonation all help to convey emotional states like 'angry', 'sad' and 'happy'. People with dysarthria, the most common speech disorder, have reduced articulatory and phonatory

This paper proposes architectures that facilitate the extrapolation of emotional expressions in deep neural network (DNN)-based text-to-speech (TTS). In this study, the meaning of “extrapolate emotional expressions” is to borrow emotional expressions from others, and the collection of emotional speech uttered by target speakers is unnecessary. Although a DNN has potential power to construct DNN-based

The improvement of the performance of online separating speech and music is an NP problem and the separation optimization increases the complexity of the method in a Robust Principal Component Analysis (RPCA) method which is time consuming in big size matrix computations. This paper presents a RPCA-based speech and music separation method to reduce the amount of computational complexity and be robust

Multimodal emotion recognition is a challenging task due to different modalities emotions expressed during a specific time in video clips. Considering the existed spatial-temporal correlation in the video, we propose an audio-visual fusion model of deep learning features with a Mixture of Brain Emotional Learning (MoBEL) model inspired by the brain limbic system. The proposed model is composed of two

Automatic speech emotion recognition (SER) by a computer is a critical component for more natural human-machine interaction. As in human-human interaction, the capability to perceive emotion correctly is essential to taking further steps in a particular situation. One issue in SER is whether it is necessary to combine acoustic features with other data such as facial expressions, text, and motion capture

The purpose of the study was to examine effects of verbalization of affective experience on heart-rate variability and emotions (HRV). We measured HRV of 35 subjects while they were viewing and verbalizing 48 affective pictures (IAPS). Our results showed that sympathetic activity was lower and parasympathetic activity higher when subjects were verbalizing unpleasant pictures than it was when they were

In this paper, we propose a new speech enhancement system using a deep neural network (DNN)-augmented colored-noise Kalman filter. In our system, both clean speech and noise are modelled as autoregressive (AR) processes, whose parameters comprise the linear prediction coefficients (LPCs) and the driving noise variances. The LPCs are obtained through training a multi-objective DNN that learns the mapping

Prosodic events such as pitch accents and phrase boundaries have various acoustic and temporal correlates that are used as features in machine learning models to automatically detect these events from speech. These features are often linguistically motivated, high-level features that are hand-crafted by experts to best represent the prosodic events to be detected or classified. An alternative approach

Accurately modelling the long-term dependencies of noisy speech is critical to the performance of a speech enhancement system. Current deep learning approaches to speech enhancement employ either a recurrent neural network (RNN) or a temporal convolutional network (TCN). However, RNNs and TCNs both demonstrate deficiencies when modelling long-term dependencies. Enter multi-head attention (MHA) — a

This paper presents Voks, a new family of digital instruments that allow for real-time control and modification of pre-recorded voice signal samples. An instrument based on Voks is made of Voks itself, the synthesis software and a given set of chironomic (hand-driven) interfaces. Rhythm can be accurately controlled thanks to a new methodology, based on syllabic control points. Timing can also be controlled

Replay attack poses a great threat to the Automatic Speaker Verification (ASV) system. This paper introduces Amplitude Modulation and Frequency Modulation-based features for replay Spoof Speech Detection (SSD) task. In this context, we propose Instantaneous Amplitude (IA) and Instantaneous Frequency (IF) features using Energy Separation Algorithm (ESA). The speech signal is passed through bandpass

This study examines the acoustic outputs in boundary coarticulation and attempts to explain the native and nonnative production differences in relation to speech planning. English consonant clusters across words produced by English (L1) and Mandarin (L2) speakers are analyzed. This study focuses on two segmental features (place of articulation and voicing) and computes three acoustic-based measurements

Automatic second language (L2) speech fluency assessment has been one of the ultimate goals of several projects aiming at designing Computer-Assisted Pronunciation Training (CAPT) tools for L2 learners. Usually, three challenges must be tackled in order to solve the issues at stake: 1) Defining fluency from a threefold interdisciplinary perspective (acoustic and perceptual phonetics, computer science

Dysarthria is a motor speech disorder leading to imprecise articulation of speech. Acoustic analysis capable of detecting and assessing articulation errors is useful in dysarthria diagnosis and therapy. Since speakers with dysarthria experience difficulty in producing rhotics due to complex articulatory gestures of these sounds, the hypothesis of the present study is that duration of the rhotic approximant

We address the problem of suppressing musical noise from speech enhanced using a short-time processing algorithm. Enhancement algorithms rely on noise statistics and errors in estimating the statistics lead to residual noise in the enhanced signal. A frequently encountered residual noise type is the so-called musical noise, which is a consequence of spurious peaks occurring at random locations in the

This paper presents text tokenization and context extraction without using language knowledge for text-to-speech (TTS) synthesis. To generate prosody, statistical parametric TTS synthesis typically requires the professional knowledge of the target language. Therefore, languages suitable for TTS synthesis are limited to only rich-resource languages. To achieve TTS synthesis without using language knowledge

In dialogue, speakers produce and perceive acoustic/prosodic turn-taking cues, which are fundamental for negotiating turn exchanges with their interlocutors. However, little of the temporal dynamics and cross-linguistic validity of these cues is known. In this work, we explore a set of acoustic/prosodic cues preceding three turn-transition types (hold, switch and backchannel) in three different languages

Nonnative sound perception might be challenging for adult listeners since they attune from a very young age to the phonological aspects of their native language and, thus, every nonnative sound is filtered through their first language. The present study investigates the perception of Greek consonants in both Consonant-Vowel (CV) and Vowel-Consonant (VC) syllable context by Russian monolingual speakers

Building dialogue systems plays an important role in modern life. amongst them, task-oriented dialogue for resolving problems in actual life is most worth exploring. Motivated by the development of end-to-end approaches, a task-oriented dialogue model based on bidirectional LSTM and self-attention mechanism is proposed. It not only makes good use of context and effectively solves the long-term dependency

Behavioral studies on concurrent vowels with shorter duration (50 ms) show that overall identification scores across fundamental frequency (F0) differences are reduced when compared to longer duration (200 ms). In this current study, we investigated the effect of shorter durations on concurrent vowel scores using the temporal responses of an auditory-nerve model (Zilany et al., 2014) with a modified

The present study investigates relationships between voice similarity ratings made by human listeners and comparison scores produced by an automatic speaker recognition system that includes phonetic, perceptually-relevant features in its modelling. The study analyses human voice similarity ratings of pairs of speech samples from unrelated speakers from an accent-controlled database (DyViS, Standard

In the existing speech enhancement methods based on deep neural network (DNN), the network architectures are not designed for speech enhancement specially, which extract local features of noisy speech in a non-causal way. In this paper, inspired by the feature calculation method based on the time–frequency correlation in the improved minima controlled recursive averaging (IMCRA), by using the long

The current study describes the collection of a new phonemically-balanced sentence resource for French, known as the Fharvard corpus. The resource consists of 700 sentences inspired by the original English Harvard sentences, along with audio recordings from one female and one male native French talker. Each of the sentences contains five mono- or bisyllabic keywords and are grouped into 70 lists of

Entrainment is the tendency of interlocutors to become more similar to each other in their way of speaking. This phenomenon has been repeatedly documented and is associated with multiple social aspects of human-human conversations. However, there is a dearth of research on the effects of spoken dialogue systems (SDSs) with implemented acoustic-prosodic (dis)entrainment policies. The goal of the present

The frequency-domain adaptive filter (FDAF) is a competitive candidate for stereo acoustic echo cancellation (SAEC) because of its good convergence and computational efficiency. However, there is a conflict between the convergence rate and the steady-state misalignment when using a constant step size. In this paper, a variable step-size approach is proposed for the stereo echo canceller in the frequency

Feature representation plays a critical role in speech emotion recognition (SER). As a method of data dimensionality reduction, Non-negative Matrix Factorization (NMF) can obtain the low-dimensional representation of data by matrix decomposition, and make the data more distinguishable. In order to improve the recognition ability of NMF for SER, we conduct a potential study on NMF and propose a supervised

Hypernasality in the speech of children with cleft palate is a consequence of velopharyngeal insufficiency. The spectral analysis of hypernasal speech shows the presence of nasal formants and anti-formants in the spectrum which affects the harmonic-intensity. The nasal formants increase whereas the anti-formants decrease the magnitude of harmonics around its location of addition. Hence, the spectrum

Research on prominence perception has made use of animated agents and controlled speech in experimental settings, but these methodologies have disregarded some aspects of the acoustic and visual correlates of prominence. To overcome these limitations we propose a new methodological approach using spontaneous speech data. For this, we created a small database with extracts from a television talent show

Speech signals may contain different paralinguistic aspects such as the presence of pathologies that affect the proper communication capabilities of a speaker. Those speech disorders have different origin depending on the type of the disease. For instance, diseases with morphological origin such as cleft lip and palate that causes hypernasality, or with neurodegenerative origin such as Parkinson’s

The validity of glottal inverse filtering (GIF) to obtain a glottal flow waveform from radiated pressure signal in the presence and absence of source-filter interaction was studied systematically. A driven vocal fold surface model of vocal fold vibration was used to generate source signals. A one-dimensional wave reflection algorithm was used to solve for acoustic pressures in the vocal tract. Several

In this paper we evaluate the accuracy, recording interference, and articulatory quality of two different ultrasound probe stabilization headsets: a metallic Ultrasound Stabilisation Headset (USH) and UltraFit, a recently developed headset that is 3D printed in Nylon. To evaluate accuracy, we recorded three native speakers of German with different head sizes using an optical marker tracking system

The speech of the individuals with cleft palate (CP) is generally characterized by the presence of abnormal nasal resonances during the production of voiced sounds, primarily in vowels, and is called hypernasality. Hypernasality is present in more than 50% of the individuals with CP, and it often results in degraded speech, both in quality and intelligibility. The current work describes the signal

The aim of the present perceptual study is to weight tones and vowels as acoustic cues in Chinese subregional dialect identification, and to test the credibility of the subregional dialect classification that has been proposed in the literature. Our findings show that listeners are able to pinpoint speakers’ subregional dialect even when only given monosyllabic Chinese word stimuli, either natural

Objective intelligibility assessment of dysarthric speech can assist clinicians in diagnosis of speech disorders as well as in medical treatment. This study investigates the use of glottal parameters (i.e. parameters that describe the acoustical excitation of voiced speech, the glottal flow) in the automatic intelligibility assessment of dysarthric speech. Instead of directly predicting the intelligibility

The electrolarynx (EL) is an electromechanical device that enables patients to produce voice following the surgical removal of their larynx. The purpose of this study is to understand the acoustic and perceptual characteristics of Mandarin consonants produced by EL speakers. First, the acoustic characteristics (including speech intensity, consonant duration, spectral peak, and F2 onset) of Mandarin

In this paper, we investigate the application of graph signal processing (GSP) theory in speech enhancement. We first propose a set of shift operators to construct graph speech signals, and then analyze their spectrum in the graph Fourier domain. By leveraging the differences between the spectrum of graph speech and graph noise signals, we further propose the graph spectral subtraction (GSS) method

Building speech synthesis systems for Indian languages is challenging owing to the fact that digital resources for these languages are hardly available. Vocabulary independent speech synthesis requires that a given text is split at the level of the smallest sound unit, namely, phone. The waveforms or models of phones are concatenated to produce speech. The waveforms corresponding to that of the phones

In this study, we propose a new concept, the gammachirp envelope distortion index (GEDI), based on the signal-to-distortion ratio in the auditory envelope, SDRenv, to predict the intelligibility of speech enhanced by nonlinear algorithms. The objective of GEDI is to calculate the distortion between enhanced and clean-speech representations in the domain of a temporal envelope extracted by the gammachirp

We present a novel study of relationships between automatic accent identification (AID) and accent-robust automatic speech recognition (ASR), using i-vector based AID and deep neural network, hidden Markov Model (DNN-HMM) based ASR. A visualization of the AID i-vector space and a novel analysis of the accent content of the WSJCAM0 corpus are presented. Accents that occur at the periphery of AID space

A deep neural network approach to voice conversion usually depends on a large amount of parallel training data from source and target speakers. In this paper, we propose a novel conversion pipeline, DeepConversion, that leverages a large amount of non-parallel, multi-speaker data, but requires only a small amount of parallel training data. It is believed that we can represent the shared characteristics

This paper presents a revised version of the Hearing-Aid Speech Perception Index (HASPI). The index is based on a model of the auditory periphery that incorporates changes due to hearing loss and is valid for both normal-hearing and hearing-impaired listeners. It is an intrusive metric that compares the time-frequency envelope and temporal fine structure (TFS) of a degraded signal to an unprocessed

Speech emotion recognition (SER) has recently been receiving increased interest due to the rapid advancements in affective computing and human computer interaction. English, German, Mandarin and Indian are among the most commonly considered languages for SER along with other European and Asian languages. However, few researches have implemented Arabic SER systems due to the scarcity of available Arabic

This paper presents a review of multi-objective deep learning methods that have been introduced in the literature for speech denoising. After stating an overview of conventional, single objective deep learning, and hybrid or combined conventional and deep learning methods, a review of the mathematical framework of the multi-objective deep learning methods for speech denoising is provided. A representative

Previous studies have suggested that female voices may impede verbal processing. For example, words were remembered less well and lexical decision was slower when spoken by a female speaker. The current study tried to replicate this gender effect in an auditory semantic/associative priming task that excluded any effects of speaker variability and extended previous research by examining the role of

This study investigates the relative contribution of L2 intonation, rhythm, and speech rate to L1 perceptions of accentedness and comprehensibility. The intonation, rhythm, and speech rate of an L1 speaker of Dutch was transferred onto the segmental string of four Spanish learners of Dutch, resulting in eight conditions that reflect all possible combinations of these prosodic cues. Our results show

In this paper, we investigate single-channel speech enhancement algorithms that operate in the short-time Fourier transform and take into account dependencies w.r.t. frequency. As a result of allowing for inter-frequency dependencies, the minimum mean square error optimal estimates of the short-time Fourier transform expansion coefficients are functions of complex-valued covariance matrices in general

Spoken documents, such as podcasts or lectures, are a growing presence in everyday life. Being able to automatically identify their discourse structure is an important step to understanding what a spoken document is about. Moreover, finer-grained units, such as paragraphs, are highly desirable for presenting and analyzing spoken content. However, little work has been done on discourse based speech

This paper builds on a multi-task Deep Neural Network (DNN), which provides an utterance-level feature representation called j-vector, to implement a Text-dependent Speaker Verification (TDSV) system. This technique exploits the speaker idiosyncrasies associated with individual pass-phrases. However, speaker information is known to be characteristic of more specific speech units and, thus, it is likely

The objectives of the dysarthria assessment are to discriminate dysarthric speech from normal speech, to estimate the severity of dysarthria in terms of the dysarthric speech intelligibility, and to find the motor speech subsystem which causes defects in speech production. In this work, analytic phase features are investigated for the objective assessment of dysarthria. In this connection, the importance