Speaker adaptation of an acoustic-to-articulatory inversion model using cascaded Gaussian mixture regressions

International audienceThe article presents a method for adapting a GMM-based acoustic-articulatory inversion model trained on a reference speaker to another speaker. The goal is to estimate the articulatory trajectories in the geometrical space of a reference speaker from the speech audio signal of another speaker. This method is developed in the context of a system of visual biofeedback, aimed at pronunciation training. This system provides a speaker with visual information about his/her own articulation, via a 3D orofacial clone. In previous work, we proposed to use GMM-based voice conversion for speaker adaptation. Acoustic-articulatory mapping was achieved in 2 consecutive steps: 1) converting the spectral trajectories of the target speaker (i.e. the system user) into spectral trajectories of the reference speaker (voice conversion), and 2) estimating the most likely articulatory trajectories of the reference speaker from the converted spectral features (acoustic-articulatory inversion). In this work, we propose to combine these two steps into the same statistical mapping framework, by fusing multiple regressions based on trajectory GMM and maximum likelihood criterion (MLE). The proposed technique is compared to two standard speaker adaptation techniques based respectively on MAP and MLLR

Parallel Reference Speaker Weighting for Kinematic-Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and open problem. This paper proposes a novel approach to acoustic-to-articulatory inversion, Parallel Reference Speaker Weighting (PRSW), which requires no kinematic data for the target speaker and a small amount of acoustic adaptation data. PRSW hypothesizes that acoustic and kinematic similarities are correlated and uses speaker-adapted articulatory models derived from acoustically derived weights. The system was assessed using a 20-speaker data set of synchronous acoustic and Electromagnetic Articulography (EMA) kinematic data. Results demonstrate that by restricting the reference group to a subset consisting of speakers with strong individual speaker-dependent inversion performance, the PRSW method is able to attain kinematic-independent acoustic-to-articulatory inversion performance nearly matching that of the speaker-dependent model, with an average correlation of 0.62 versus 0.63. This indicates that given a sufficiently complete and appropriately selected reference speaker set for adaptation, it is possible to create effective articulatory models without kinematic training data

Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping

International audienceThis article addresses the adaptation of an acoustic-articulatory inversion model of a reference speaker to the voice of another source speaker, using a limited amount of audio-only data. In this study, the articulatory-acoustic relationship of the reference speaker is modeled by a Gaussian mixture model and inference of articulatory data from acoustic data is made by the associated Gaussian mixture regression (GMR). To address speaker adaptation, we previously proposed a general framework called Cascaded-GMR (C-GMR) which decomposes the adaptation process into two consecutive steps: spectral conversion between source and reference speaker and acoustic-articulatory inversion of converted spectral trajectories. In particular, we proposed the Integrated C-GMR technique (IC-GMR) in which both steps are tied together in the same probabilistic model. In this article, we extend the C-GMR framework with another model called Joint-GMR (J-GMR). Contrary to the IC-GMR, this model aims at exploiting all potential acoustic-articulatory relationships, including those between the source speaker's acoustics and the reference speaker's articulation. We present the full derivation of the exact Expectation-Maximization (EM) training algorithm for the J-GMR. It exploits the missing data methodology of machine learning to deal with limited adaptation data. We provide an extensive evaluation of the J-GMR on both synthetic acoustic-articulatory data and on the multi-speaker MOCHA EMA database. We compare the J-GMR performance to other models of the C-GMR framework, notably the IC-GMR, and discuss their respective merits

A speaker adaptive DNN training approach for speaker-independent acoustic inversion

We address the speaker-independent acoustic inversion (AI) problem, also referred to as acoustic-to-articulatory mapping. The scarce availability of multi-speaker articulatory data makes it difficult to learn a mapping which generalizes from a limited number of training speakers and reliably reconstructs the articulatory movements of unseen speakers. In this paper, we propose a Multi-task Learning (MTL)-based approach that explicitly separates the modeling of each training speaker AI peculiarities from the modeling of AI characteristics that are shared by all speakers. Our approach stems from the well known Regularized MTL approach and extends it to feed-forward deep neural networks (DNNs). Given multiple training speakers, we learn for each an acoustic-to-articulatory mapping represented by a DNN. Then, through an iterative procedure, we search for a canonical speaker-independent DNN that is "similar" to all speaker-dependent DNNs. The degree of similarity is controlled by a regularization parameter. We report experiments on the University of Wisconsin X-ray Microbeam Database under different training/testing experimental settings. The results obtained indicate that our MTL-trained canonical DNN largely outperforms a standardly trained (i.e., single task learning-based) speaker independent DNN

Speaker Independent Acoustic-to-Articulatory Inversion

Acoustic-to-articulatory inversion, the determination of articulatory parameters from acoustic signals, is a difficult but important problem for many speech processing applications, such as automatic speech recognition (ASR) and computer aided pronunciation training (CAPT). In recent years, several approaches have been successfully implemented for speaker dependent models with parallel acoustic and kinematic training data. However, in many practical applications inversion is needed for new speakers for whom no articulatory data is available. In order to address this problem, this dissertation introduces a novel speaker adaptation approach called Parallel Reference Speaker Weighting (PRSW), based on parallel acoustic and articulatory Hidden Markov Models (HMM). This approach uses a robust normalized articulatory space and palate referenced articulatory features combined with speaker-weighted adaptation to form an inversion mapping for new speakers that can accurately estimate articulatory trajectories. The proposed PRSW method is evaluated on the newly collected Marquette electromagnetic articulography - Mandarin Accented English (EMA-MAE) corpus using 20 native English speakers. Cross-speaker inversion results show that given a good selection of reference speakers with consistent acoustic and articulatory patterns, the PRSW approach gives good speaker independent inversion performance even without kinematic training data