Articulatory-to-acoustic (forward) mapping is a technique to predict speech using various articulatory acquisition techniques (e.g. ultrasound tongue imaging, lip video). Real-time MRI (rtMRI) of the vocal tract has not been used before for this purpose. The advantage of MRI is that it has a high `relative' spatial resolution: it can capture not only lingual, labial and jaw motion, but also the velum and the pharyngeal region, which is typically not possible with other techniques. In the current paper, we train various DNNs (fully connected, convolutional and recurrent neural networks) for articulatory-to-speech conversion, using rtMRI as input, in a speaker-specific way. We use two male and two female speakers of the USC-TIMIT articulatory database, each of them uttering 460 sentences. We evaluate the results with objective (Normalized MSE and MCD) and subjective measures (perceptual test) and show that CNN-LSTM networks are preferred which take multiple images as input, and achieve MCD scores between 2.8-4.5 dB. In the experiments, we find that the predictions of speaker `m1' are significantly weaker than other speakers. We show that this is caused by the fact that 74% of the recordings of speaker `m1' are out of sync.
Budapest University of Technology and Economics（布达佩斯技术与经济大学）
Acoustic-to-articulatory inversion (AAI) methods estimate articulatory movements from the acoustic speech signal, which can be useful in several tasks such as speech recognition, synthesis, talking heads and language tutoring. Most earlier inversion studies are based on point-tracking articulatory techniques (e.g. EMA or XRMB). The advantage of rtMRI is that it provides dynamic information about the full midsagittal plane of the upper airway, with a high 'relative' spatial resolution. In this work, we estimated midsagittal rtMRI images of the vocal tract for speaker dependent AAI, using MGC-LS…
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Grenoble INP（格勒诺布尔国立理工学院）；GIPSA-lab（格勒诺布尔图像语音信号自动化实验室）；Univ. Savoie Mont Blanc（萨瓦勃朗峰大学）；LPNC（认知神经科学实验室）
We propose a computational model of speech production combining a pre-trained neural articulatory synthesizer able to reproduce complex speech stimuli from a limited set of interpretable articulatory parameters, a DNN-based internal forward model predicting the sensory consequences of articulatory commands, and an internal inverse model based on a recurrent neural network recovering articulatory commands from the acoustic speech input. Both forward and inverse models are jointly trained in a self-supervised way from raw acoustic-only speech data from different speakers. The imitation simulatio…
This work presents our advancements in controlling an articulatory speech synthesis engine, \textit{viz.}, Pink Trombone, with hand gestures. Our interface translates continuous finger movements and wrist flexion into continuous speech using vocal tract area-function based articulatory speech synthesis. We use Cyberglove II with 18 sensors to capture the kinematic information of the wrist and the individual fingers, in order to control a virtual tongue. The coordinates and the bending values of the sensors are then utilized to fit a spline tongue model that smoothens out the noisy values and o…
Interpretable Modeling of Articulatory Temporal Dynamics from real-time MRI for Phoneme Recognition
Signal Analysis and Interpretation Lab, University of Southern California（南加州大学信号分析与解释实验室）；Department of Linguistics, University of Southern California（南加州大学语言学系）
The articulatory geometric configurations of the vocal tract and the acoustic properties of the resultant speech sound are considered to have a strong causal relationship. This paper aims at finding a joint latent representation between the articulatory and acoustic domain for vowel sounds via invertible neural network models, while simultaneously preserving the respective domain-specific features. Our model utilizes a convolutional autoencoder architecture and normalizing flow-based models to allow both forward and inverse mappings in a semi-supervised manner, between the mid-sagittal vocal t…
Faculty of Electrical Engineering and Information Technologies, CMUS（斯科普里西里尔和迪乌斯大学电气工程与信息技术学院）；University College London（伦敦大学学院）；TU Dresden（德累斯顿工业大学）
The way infants use auditory cues to learn to speak despite the acoustic mismatch of their vocal apparatus is a hot topic of scientific debate. The simulation of early vocal learning using articulatory speech synthesis offers a way towards gaining a deeper understanding of this process. One of the crucial parameters in these simulations is the choice of features and a metric to evaluate the acoustic error between the synthesised sound and the reference target. We contribute with evaluating the performance of a set of 40 feature-metric combinations for the task of optimising the production of s…
ARTI-6: Towards Six-dimensional Articulatory Speech Encoding
Signal Analysis and Interpretation Lab, University of Southern California（南加州大学信号分析与解释实验室）；Department of Linguistics, University of Southern California（南加州大学语言学系）
It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representation could improve the performances of a deep generative model (here a variational autoencoder) trained to encode and decode acoustic speech features. First we develop an articulatory model able to associate articulatory parameters describing the jaw, tongue, lips and velum configurations with vocal tract shapes and spectral features. Then we incorporate these articulatory parameters into a variational autoencoder appl…
University of Michigan（密歇根大学）；Northwestern University（西北大学）；Michigan Medicine（密歇根医学中心）
Huntington Disease (HD) is a progressive disorder which often manifests in motor impairment. Motor severity (captured via motor score) is a key component in assessing overall HD severity. However, motor score evaluation involves in-clinic visits with a trained medical professional, which are expensive and not always accessible. Speech analysis provides an attractive avenue for tracking HD severity because speech is easy to collect remotely and provides insight into motor changes. HD speech is typically characterized as having irregular articulation. With this in mind, acoustic features that ca…
We present an interface involving four degrees-of-freedom (DOF) mechanical control of a two dimensional, mid-sagittal tongue through a biomechanical toolkit called ArtiSynth and a sound synthesis engine called JASS towards articulatory sound synthesis. As a demonstration of the project, the user will learn to produce a range of JASS vocal sounds, by varying the shape and position of the ArtiSynth tongue in 2D space through a set of four force-based sensors. In other words, the user will be able to physically play around with these four sensors, thereby virtually controlling the magnitude of fo…
University of California, Berkeley（加州大学伯克利分校）；Carnegie Mellon University（卡内基梅隆大学）；University of Southern California（南加州大学）
In the articulatory synthesis task, speech is synthesized from input features containing information about the physical behavior of the human vocal tract. This task provides a promising direction for speech synthesis research, as the articulatory space is compact, smooth, and interpretable. Current works have highlighted the potential for deep learning models to perform articulatory synthesis. However, it remains unclear whether these models can achieve the efficiency and fidelity of the human speech production system. To help bridge this gap, we propose a time-domain articulatory synthesis me…
Institute for Systems Research, University of Maryland College Park（马里兰大学帕克分校系统研究所）
In this work, we incorporated acoustically derived source features, aperiodicity, periodicity and pitch as additional targets to an acoustic-to-articulatory speech inversion (SI) system. We also propose a Temporal Convolution based SI system, which uses auditory spectrograms as the input speech representation, to learn long-range dependencies and complex interactions between the source and vocal tract, to improve the SI task. The experiments are conducted with both the Wisconsin X-ray microbeam (XRMB) and Haskins Production Rate Comparison (HPRC) datasets, with comparisons done with respect to…
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；Institute of Information Science, Academia Sinica（中央研究院资讯科学研究所）；Graduate School of Informatics, Nagoya University（名古屋大学信息学研究科）；National Institute of Information and Communications Technology（国立信息通信技术研究所）
Synthesized speech from articulatory movements can have real-world use for patients with vocal cord disorders, situations requiring silent speech, or in high-noise environments. In this work, we present EMA2S, an end-to-end multimodal articulatory-to-speech system that directly converts articulatory movements to speech signals. We use a neural-network-based vocoder combined with multimodal joint-training, incorporating spectrogram, mel-spectrogram, and deep features. The experimental results confirm that the multimodal approach of EMA2S outperforms the baseline system in terms of both objectiv…
Open-Source Manually Annotated Vocal Tract Database for Automatic Segmentation from 3D MRI Using Deep Learning: Benchmarking 2D and 3D Convolutional and Transformer Networks
University of Iowa（爱荷华大学）；Shenandoah University（谢南多厄大学）
Georgia State University（佐治亚州立大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
From crying to babbling and then to speech, infant's vocal tract goes through anatomic restructuring. In this paper, we propose a non-invasive fast method of using infant cry signals with convolutional neural network (CNN) based age classification to diagnose the abnormality of the vocal tract development as early as 4-month age. We study F0, F1, F2, and spectrograms and relate them to the postnatal development of infant vocalization. A novel CNN based age classification is performed with binary age pairs to discover the pattern and tendency of the vocal tract changes. The effectiveness of thi…
The simulation of two-dimensional (2D) wave propagation is an affordable computational task and its use can potentially improve time performance in vocal tracts' acoustic analysis. Several models have been designed that rely on 2D wave solvers and include 2D representations of three-dimensional (3D) vocal tract-like geometries. However, until now, only the acoustics of straight 3D tubes with circular cross-sections have been successfully replicated with this approach. Furthermore, the simulation of the resulting 2D shapes requires extremely high spatio-temporal resolutions, dramatically reduci…
Speech sounds of spoken language are obtained by varying configuration of the articulators surrounding the vocal tract. They contain abundant information that can be utilized to better understand the underlying mechanism of human speech production. We propose a novel deep neural network-based learning framework that understands acoustic information in the variable-length sequence of vocal tract shaping during speech production, captured by real-time magnetic resonance imaging (rtMRI), and translate it into text. The proposed framework comprises of spatiotemporal convolutions, a recurrent netwo…
This study investigates the speech articulatory coordination in schizophrenia subjects exhibiting strong positive symptoms (e.g.hallucinations and delusions), using a time delay embedded correlation analysis. We show that the schizophrenia subjects with strong positive symptoms and who are markedly ill pose complex coordination patterns in facial and speech gestures than what is observed in healthy subjects. This observation is in contrast to what previous studies have shown in Major Depressive Disorder (MDD), where subjects with MDD show a simpler coordination pattern with respect to healthy …
Vocal tract configurations play a vital role in generating distinguishable speech sounds, by modulating the airflow and creating different resonant cavities in speech production. They contain abundant information that can be utilized to better understand the underlying speech production mechanism. As a step towards automatic mapping of vocal tract shape geometry to acoustics, this paper employs effective video action recognition techniques, like Long-term Recurrent Convolutional Networks (LRCN) models, to identify different vowel-consonant-vowel (VCV) sequences from dynamic shaping of the voca…
University of British Columbia（不列颠哥伦比亚大学）；Northeastern University（东北大学）
The two-dimensional (2D) numerical approaches for vocal tract (VT) modelling can afford a better balance between the low computational cost and accurate rendering of acoustic wave propagation. However, they require a high spatio-temporal resolution in the numerical scheme for a precise estimation of acoustic formants at the simulation run-time expense. We have recently proposed a new VT acoustic modelling technique, known as the 2.5D Finite-Difference Time-Domain (2.5D FDTD), which extends the existing 2D FDTD approach by adding tube depth to its acoustic wave solver. In this work, first, the …
University of Pennsylvania（宾夕法尼亚大学）；Linguistic Data Consortium（语言数据联盟）
Fundamental frequency (F0) has long been treated as the physical definition of "pitch" in phonetic analysis. But there have been many demonstrations that F0 is at best an approximation to pitch, both in production and in perception: pitch is not F0, and F0 is not pitch. Changes in the pitch involve many articulatory and acoustic covariates; pitch perception often deviates from what F0 analysis predicts; and in fact, quasi-periodic signals from a single voice source are often incompletely characterized by an attempt to define a single time-varying F0. In this paper, we find strong support for t…
Institute for Systems Research, University of Maryland College Park（马里兰大学帕克分校系统研究所）；Pindrop Inc.（平卓普公司）
Multi-resolution spectro-temporal features of a speech signal represent how the brain perceives sounds by tuning cortical cells to different spectral and temporal modulations. These features produce a higher dimensional representation of the speech signals. The purpose of this paper is to evaluate how well the auditory cortex representation of speech signals contribute to estimate articulatory features of those corresponding signals. Since obtaining articulatory features from acoustic features of speech signals has been a challenging topic of interest for different speech communities, we inves…
Articulatory distinctive features, as well as phonetic transcription, play important role in speech-related tasks: computer-assisted pronunciation training, text-to-speech conversion (TTS), studying speech production mechanisms, speech recognition for low-resourced languages. End-to-end approaches to speech-related tasks got a lot of traction in recent years. We apply Listen, Attend and Spell~(LAS)~\cite{Chan-LAS2016} architecture to phones recognition on a small small training set, like TIMIT~\cite{TIMIT-1992}. Also, we introduce a novel decoding technique that allows to train manners and pla…
The various speech sounds of a language are obtained by varying the shape and position of the articulators surrounding the vocal tract. Analyzing their variations is crucial for understanding speech production, diagnosing speech disorders and planning therapy. Identifying key anatomical landmarks of these structures on medical images is a pre-requisite for any quantitative analysis and the rising amount of data generated in the field calls for an automatic solution. The challenge lies in the high inter- and intra-speaker variability, the mutual interaction between the articulators and the mode…
The coupling of vocal fold (source) and vocal tract (filter) is one of the most critical factors in source-filter articulation theory. The traditional linear source-filter theory has been challenged by current research which clearly shows the impact of acoustic loading on the dynamic behavior of the vocal fold vibration as well as the variations in the glottal flow pulses shape. This paper outlines the underlying mechanism of source-filter interactions; demonstrates the design and working principles of coupling for the various existing vocal cord and vocal tract biomechanical models. For our s…
Depression detection using vocal biomarkers is a highly researched area. Articulatory coordination features (ACFs) are developed based on the changes in neuromotor coordination due to psychomotor slowing, a key feature of Major Depressive Disorder. However findings of existing studies are mostly validated on a single database which limits the generalizability of results. Variability across different depression databases adversely affects the results in cross corpus evaluations (CCEs). We propose to develop a generalized classifier for depression detection using a dilated Convolutional Neural N…
The estimation of glottal flow from a speech waveform is a key method for speech analysis and parameterization. Significant research effort has been made to dissociate the first vocal tract resonance from the glottal formant (the low-frequency resonance describing the open-phase of the vocal fold vibration). However few methods cope with estimation of high-frequency spectral tilt to describe the return-phase of the vocal fold vibration, which is crucial to the perception of vocal effort. This paper proposes an improved version of the well-known Iterative Adaptive Inverse Filtering (IAIF) calle…
