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

Improving the Sampling of the Null Space of the Acoustic-to-Articulatory Mapping

International audienceThis paper presents a new method for sampling the null space of the acoustic-to-articulatory mapping, which is considerably faster and more accurate than the previous method presented by Ouni and Laprie. This is achieved by using a simple stochastic exploration of the articulatory space instead of complex linear programming techniques. This new method allows for a much faster and more accurate inversion process

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

Investigating Non-Uniqueness in the Acoustic-Articulatory Inversion Mapping

The task of inferring articulatory configurations from a given acoustic signal is a problem for which a reliable and accurate solution has been lacking for a number of decades. The changing shape of the vocal-tract is responsible for altering the parameters of sound. Each different configuration of articulators will regularly lead to a single distinct sound being produced (a unique mapping from the articulator to the acoustics). Therefore, it should be possible to take an acoustic signal and invert the process, giving the exact vocal-tract shape for a given sound. This would have wide-reaching applications in the field of speech and language technology, such as in improving facial animation and speech recognition systems. Using vocal-tract information inferred from the acoustic signal can facilitate a richer understanding of the actual constraints in articulator movement. However, research concerned with the inversion mapping has revealed that there is often a multi-valued mapping from the acoustic domain to the articulatory domain. Work in identifying and resolving this non-uniqueness thus far has been somewhat successful, with Mixture-Density Networks (MDN) and articulator trajectory systems presenting probabilistic methods of finding the most likely articulatory configuration for a given signal. Using an subset of an EMA corpus, along with a combination of an instantaneous inversion mapping and a non-parametric clustering algorithm, I aim to quantify the extent to which acoustically similar vectors to a given phone can exhibit qualitatively different vocal-tract shapes. Categorical identification of acoustically similar sounds that can have shown a multi-valued mapping in the articulatory domain, as well as identifying which articulators this occurs for, could be key to resolving issues in the reliability and quality of the inversion mapping

ARTICULATORY INFORMATION FOR ROBUST SPEECH RECOGNITION

Current Automatic Speech Recognition (ASR) systems fail to perform nearly as good as human speech recognition performance due to their lack of robustness against speech variability and noise contamination. The goal of this dissertation is to investigate these critical robustness issues, put forth different ways to address them and finally present an ASR architecture based upon these robustness criteria. Acoustic variations adversely affect the performance of current phone-based ASR systems, in which speech is modeled as `beads-on-a-string', where the beads are the individual phone units. While phone units are distinctive in cognitive domain, they are varying in the physical domain and their variation occurs due to a combination of factors including speech style, speaking rate etc.; a phenomenon commonly known as `coarticulation'. Traditional ASR systems address such coarticulatory variations by using contextualized phone-units such as triphones. Articulatory phonology accounts for coarticulatory variations by modeling speech as a constellation of constricting actions known as articulatory gestures. In such a framework, speech variations such as coarticulation and lenition are accounted for by gestural overlap in time and gestural reduction in space. To realize a gesture-based ASR system, articulatory gestures have to be inferred from the acoustic signal. At the initial stage of this research an initial study was performed using synthetically generated speech to obtain a proof-of-concept that articulatory gestures can indeed be recognized from the speech signal. It was observed that having vocal tract constriction trajectories (TVs) as intermediate representation facilitated the gesture recognition task from the speech signal. Presently no natural speech database contains articulatory gesture annotation; hence an automated iterative time-warping architecture is proposed that can annotate any natural speech database with articulatory gestures and TVs. Two natural speech databases: X-ray microbeam and Aurora-2 were annotated, where the former was used to train a TV-estimator and the latter was used to train a Dynamic Bayesian Network (DBN) based ASR architecture. The DBN architecture used two sets of observation: (a) acoustic features in the form of mel-frequency cepstral coefficients (MFCCs) and (b) TVs (estimated from the acoustic speech signal). In this setup the articulatory gestures were modeled as hidden random variables, hence eliminating the necessity for explicit gesture recognition. Word recognition results using the DBN architecture indicate that articulatory representations not only can help to account for coarticulatory variations but can also significantly improve the noise robustness of ASR system

Statistical Parametric Methods for Articulatory-Based Foreign Accent Conversion

Foreign accent conversion seeks to transform utterances from a non-native speaker (L2) to appear as if they had been produced by the same speaker but with a native (L1) accent. Such accent-modified utterances have been suggested to be effective in pronunciation training for adult second language learners. Accent modification involves separating the linguistic gestures and voice-quality cues from the L1 and L2 utterances, then transposing them across the two speakers. However, because of the complex interaction between these two sources of information, their separation in the acoustic domain is not straightforward. As a result, vocoding approaches to accent conversion results in a voice that is different from both the L1 and L2 speakers. In contrast, separation in the articulatory domain is straightforward since linguistic gestures are readily available via articulatory data. However, because of the difficulty in collecting articulatory data, conventional synthesis techniques based on unit selection are ill-suited for accent conversion given the small size of articulatory corpora and the inability to interpolate missing native sounds in L2 corpus. To address these issues, this dissertation presents two statistical parametric methods to accent conversion that operate in the acoustic and articulatory domains, respectively. The acoustic method uses a cross-speaker statistical mapping to generate L2 acoustic features from the trajectories of L1 acoustic features in a reference utterance. Our results show significant reductions in the perceived non-native accents compared to the corresponding L2 utterance. The results also show a strong voice-similarity between accent conversions and the original L2 utterance. Our second (articulatory-based) approach consists of building a statistical parametric articulatory synthesizer for a non-native speaker, then driving the synthesizer with the articulators from the reference L1 speaker. This statistical approach not only has low data requirements but also has the flexibility to interpolate missing sounds in the L2 corpus. In a series of listening tests, articulatory accent conversions were rated more intelligible and less accented than their L2 counterparts. In the final study, we compare the two approaches: acoustic and articulatory. Our results show that the articulatory approach, despite the direct access to the native linguistic gestures, is less effective in reducing perceived non-native accents than the acoustic approach

Modelling Speech Dynamics with Trajectory-HMMs

Institute for Communicating and Collaborative SystemsThe conditional independence assumption imposed by the hidden Markov models (HMMs) makes it difficult to model temporal correlation patterns in human speech. Traditionally, this limitation is circumvented by appending the first and second-order regression coefficients to the observation feature vectors. Although this leads to improved performance in recognition tasks, we argue that a straightforward use of dynamic features in HMMs will result in an inferior model, due to the incorrect handling of dynamic constraints. In this thesis I will show that an HMM can be transformed into a Trajectory-HMM capable of generating smoothed output mean trajectories, by performing a per-utterance normalisation. The resulting model can be trained by either maximisingmodel log-likelihood or minimisingmean generation errors on the training data. To combat the exponential growth of paths in searching, the idea of delayed path merging is proposed and a new time-synchronous decoding algorithm built on the concept of token-passing is designed for use in the recognition task. The Trajectory-HMM brings a new way of sharing knowledge between speech recognition and synthesis components, by tackling both problems in a coherent statistical framework. I evaluated the Trajectory-HMM on two different speech tasks using the speaker-dependent MOCHA-TIMIT database. First as a generative model to recover articulatory features from speech signal, where the Trajectory-HMM was used in a complementary way to the conventional HMM modelling techniques, within a joint Acoustic-Articulatory framework. Experiments indicate that the jointly trained acoustic-articulatory models are more accurate (having a lower Root Mean Square error) than the separately trained ones, and that Trajectory-HMM training results in greater accuracy compared with conventional Baum-Welch parameter updating. In addition, the Root Mean Square (RMS) training objective proves to be consistently better than the Maximum Likelihood objective. However, experiment of the phone recognition task shows that the MLE trained Trajectory-HMM, while retaining attractive properties of being a proper generative model, tends to favour over-smoothed trajectories among competing hypothesises, and does not perform better than a conventional HMM. We use this to build an argument that models giving a better fit on training data may suffer a reduction of discrimination by being too faithful to the training data. Finally, experiments on using triphone models show that increasing modelling detail is an effective way to leverage modelling performance with little added complexity in training