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 simulations are evaluated objectively and subjectively and display quite encouraging performances.
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…
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…
Budapest University of Technology and Economics（布达佩斯技术与经济大学）；MTA-ELTE Lendület Lingual Articulation Research Group（MTA-ELTE伦杜莱特语言发音研究组）
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 art…
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…
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…
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）
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…
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…
UC 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）
Articulatory representation learning is the fundamental research in modeling neural speech production system. Our previous work has established a deep paradigm to decompose the articulatory kinematics data into gestures, which explicitly model the phonological and linguistic structure encoded with human speech production mechanism, and corresponding gestural scores. We continue with this line of work by raising two concerns: (1) The articulators are entangled together in the original algorithm such that some of the articulators do not leverage effective moving patterns, which limits the interp…
Articulatory strategy as a source of variation in acoustic vowel dynamics
Linguistics and English Language, University of Manchester, United Kingdom（曼彻斯特大学语言学与英语语言系，英国）；Linguistics and English Language, Lancaster University, United Kingdom（兰卡斯特大学语言学与英语语言系，英国）
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…
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…
