Relying on critical articulators to estimate vocal tract spectra in an articulatory-acoustic database

We present a new phone-dependent feature weighting scheme that can be used to map articulatory configurations (e.g. EMA) onto vocal tract spectra (e.g. MFCC) through table lookup. The approach consists of assigning feature weights according to a feature's ability to predict the acoustic distance between frames. Since an articulator's predictive accuracy is phone-dependent (e.g., lip location is a better predictor for bilabial sounds than for palatal sounds), a unique weight vector is found for each phone. Inspection of the weights reveals a correspondence with the expected critical articulators for many phones. The proposed method reduces overall cepstral error by 6\% when compared to a uniform weighting scheme. Vowels show the greatest benefit, though improvements occur for 80\% of the tested phones

An analysis-by-synthesis approach to vocal tract modeling for robust speech recognition

In this thesis we present a novel approach to speech recognition that incorporates knowledge of the speech production process. The major contribution is the development of a speech recognition system that is motivated by the physical generative process of speech, rather than the purely statistical approach that has been the basis for virtually all current recognizers. We follow an analysis-by-synthesis approach. We begin by attributing a physical meaning to the inner states of the recognition system pertaining to the configurations the human vocal tract takes over time. We utilize a geometric model of the vocal tract, adapt it to our speakers, and derive realistic vocal tract shapes from electromagnetic articulograph (EMA) measurements in the MOCHA database. We then synthesize speech from the vocal tract configurations using a physiologically-motivated articulatory synthesis model of speech generation. Finally, the observation probability of the Hidden Markov Model (HMM) used for phone classification is a function of the distortion between the speech synthesized from the vocal tract configurations and the real speech. The output of each state in the HMM is based on a mixture of density functions

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

Precise Estimation of Vocal Tract and Voice Source Characteristics

This thesis addresses the problem of quality degradation in speech produced by parameter-based speech synthesis, within the framework of an articulatory-acoustic forward mapping. I first investigate current problems in speech parameterisation, and point out the fact that conventional parameterisation inaccurately extracts the vocal tract response due to interference from the harmonic structure of voiced speech. To overcome this problem, I introduce a method for estimating filter responses more precisely from periodic signals. The method achieves such estimation in the frequency domain by approximating all the harmonics observed in several frames based on a least squares criterion. It is shown that the proposed method is capable of estimating the response more accurately than widely-used frame-by-frame parameterisation, for simulations using synthetic speech and for an articulatory-acoustic mapping using actual speech. I also deal with the source-filter separation problem and independent control of the voice source characteristic during speech synthesis. I propose a statistical approach to separating out the vocal-tract filter response from the voice source characteristic using a large articulatory database. The approach realises such separation for voiced speech using an iterative approximation procedure under the assumption that the speech production process is a linear system composed of a voice source and a vocal-tract filter, and that each of the components is controlled independently by different sets of factors. Experimental results show that controlling the source characteristic greatly improves the accuracy of the articulatory-acoustic mapping, and that the spectral variation of the source characteristic is evidently influenced by the fundamental frequency or the power of speech. The thesis provides more accurate acoustical approximation of the vocal tract response, which will be beneficial in a wide range of speech technologies, and lays the groundwork in speech science for a new type of corpus-based statistical solution to the source-filter separation problem

Articulatory Copy Synthesis Based on the Speech Synthesizer VocalTractLab

Articulatory copy synthesis (ACS), a subarea of speech inversion, refers to the reproduction of natural utterances and involves both the physiological articulatory processes and their corresponding acoustic results. This thesis proposes two novel methods for the ACS of human speech using the articulatory speech synthesizer VocalTractLab (VTL) to address or mitigate the existing problems of speech inversion, such as non-unique mapping, acoustic variation among different speakers, and the time-consuming nature of the process. The first method involved finding appropriate VTL gestural scores for given natural utterances using a genetic algorithm. It consisted of two steps: gestural score initialization and optimization. In the first step, gestural scores were initialized using the given acoustic signals with speech recognition, grapheme-to-phoneme (G2P), and a VTL rule-based method for converting phoneme sequences to gestural scores. In the second step, the initial gestural scores were optimized by a genetic algorithm via an analysis-by-synthesis (ABS) procedure that sought to minimize the cosine distance between the acoustic features of the synthetic and natural utterances. The articulatory parameters were also regularized during the optimization process to restrict them to reasonable values. The second method was based on long short-term memory (LSTM) and convolutional neural networks, which were responsible for capturing the temporal dependence and the spatial structure of the acoustic features, respectively. The neural network regression models were trained, which used acoustic features as inputs and produced articulatory trajectories as outputs. In addition, to cover as much of the articulatory and acoustic space as possible, the training samples were augmented by manipulating the phonation type, speaking effort, and the vocal tract length of the synthetic utterances. Furthermore, two regularization methods were proposed: one based on the smoothness loss of articulatory trajectories and another based on the acoustic loss between original and predicted acoustic features. The best-performing genetic algorithms and convolutional LSTM systems (evaluated in terms of the difference between the estimated and reference VTL articulatory parameters) obtained average correlation coefficients of 0.985 and 0.983 for speaker-dependent utterances, respectively, and their reproduced speech achieved recognition accuracies of 86.25% and 64.69% for speaker-independent utterances of German words, respectively. When applied to German sentence utterances, as well as English and Mandarin Chinese word utterances, the neural network based ACS systems achieved recognition accuracies of 73.88%, 52.92%, and 52.41%, respectively. The results showed that both of these methods not only reproduced the articulatory processes but also reproduced the acoustic signals of reference utterances. Moreover, the regularization methods led to more physiologically plausible articulatory processes and made the estimated articulatory trajectories be more articulatorily preferred by VTL, thus reproducing more natural and intelligible speech. This study also found that the convolutional layers, when used in conjunction with batch normalization layers, automatically learned more distinctive features from log power spectrograms. Furthermore, the neural network based ACS systems trained using German data could be generalized to the utterances of other languages

Articulatory-based Speech Processing Methods for Foreign Accent Conversion

The objective of this dissertation is to develop speech processing methods that enable without altering their identity. We envision accent conversion primarily as a tool for pronunciation training, allowing non-native speakers to hear their native-accented selves. With this application in mind, we present two methods of accent conversion. The first assumes that the voice quality/identity of speech resides in the glottal excitation, while the linguistic content is contained in the vocal tract transfer function. Accent conversion is achieved by convolving the glottal excitation of a non-native speaker with the vocal tract transfer function of a native speaker. The result is perceived as 60 percent less accented, but it is no longer identified as the same individual. The second method of accent conversion selects segments of speech from a corpus of non-native speech based on their acoustic or articulatory similarity to segments from a native speaker. We predict that articulatory features provide a more speaker-independent representation of speech and are therefore better gauges of linguistic similarity across speakers. To test this hypothesis, we collected a custom database containing simultaneous recordings of speech and the positions of important articulators (e.g. lips, jaw, tongue) for a native and non-native speaker. Resequencing speech from a non-native speaker based on articulatory similarity with a native speaker achieved a 20 percent reduction in accent. The approach is particularly appealing for applications in pronunciation training because it modifies speech in a way that produces realistically achievable changes in accent (i.e., since the technique uses sounds already produced by the non-native speaker). A second contribution of this dissertation is the development of subjective and objective measures to assess the performance of accent conversion systems. This is a difficult problem because, in most cases, no ground truth exists. Subjective evaluation is further complicated by the interconnected relationship between accent and identity, but modifications of the stimuli (i.e. reverse speech and voice disguises) allow the two components to be separated. Algorithms to measure objectively accent, quality, and identity are shown to correlate well with their subjective counterparts

The Effect of Speaking Rate on Vowel Variability Based on the Uncontrolled Manifold Approach and Flow-Based Invertible Neural Network Modeling

Variability is intrinsic to human speech production. One approach to understand variability in speech is to decompose it into task-irrelevant (“good”) and task-relevant (“bad”) parts with respect to speech tasks. Based on the uncontrolled manifold (UCM) approach, this dissertation investigates how vowel token-to-token variability in articulation and acoustics can be decomposed into “good” and “bad” parts and how speaking rate changes the pattern of these two from the Haskins IEEE rate comparison database. Furthermore, it is examined whether the “good” part of variability, or flexibility, can be modeled directly from speech data using the flow-based invertible neural networks framework. The application of the UCM analysis and FlowINN modeling method is discussed, particularly focusing on how the “good” part of variability in speech can be useful rather than being disregarded as noise

Developing Sparse Representations for Anchor-Based Voice Conversion

Voice conversion is the task of transforming speech from one speaker to sound as if it was produced by another speaker, changing the identity while retaining the linguistic content. There are many methods for performing voice conversion, but oftentimes these methods have onerous training requirements or fail in instances where one speaker has a nonnative accent. To address these issues, this dissertation presents and evaluates a novel “anchor-based” representation of speech that separates speaker content from speaker identity by modeling how speakers form English phonemes. We call the proposed method Sparse, Anchor-Based Representation of Speech (SABR), and explore methods for optimizing the parameters of this model in native-to-native and native-to-nonnative voice conversion contexts. We begin the dissertation by demonstrating how sparse coding in combination with a compact, phoneme-based dictionary can be used to separate speaker identity from content in objective and subjective tests. The formulation of the representation then presents several research questions. First, we propose a method for improving the synthesis quality by using the sparse coding residual in combination with a frequency warping algorithm to convert the residual from the source to target speaker’s space, and add it to the target speaker’s estimated spectrum. Experimentally, we find that synthesis quality is significantly improved via this transform. Second, we propose and evaluate two methods for selecting and optimizing SABR anchors in native-to-native and native-to-nonnative voice conversion. We find that synthesis quality is significantly improved by the proposed methods, especially in native-to- nonnative voice conversion over baseline algorithms. In a detailed analysis of the algorithms, we find they focus on phonemes that are difficult for nonnative speakers of English or naturally have multiple acoustic states. Following this, we examine methods for adding in temporal constraints to SABR via the Fused Lasso. The proposed method significantly reduces the inter-frame variance in the sparse codes over other methods that incorporate temporal features into sparse coding representations. Finally, in a case study, we examine the use of the SABR methods and optimizations in the context of a computer aided pronunciation training system for building “Golden Speakers”, or ideal models for nonnative speakers of a second language to learn correct pronunciation. Under the hypothesis that the optimal “Golden Speaker” was the learner’s voice, synthesized with a native accent, we used SABR to build voice models for nonnative speakers and evaluated the resulting synthesis in terms of quality, identity, and accentedness. We found that even when deployed in the field, the SABR method generated synthesis with low accentedness and similar acoustic identity to the target speaker, validating the use of the method for building “golden speakers”

A Review of the Assessment Methods of Voice Disorders in the Context of Parkinson's Disease

In recent years, a significant progress in the field of research dedicated to the treatment of disabilities has been witnessed. This is particularly true for neurological diseases, which generally influence the system that controls the execution of learned motor patterns. In addition to its importance for communication with the outside world and interaction with others, the voice is a reflection of our personality, moods and emotions. It is a way to provide information on health status, shape, intentions, age and even the social environment. It is also a working tool for many, but an important element of life for all. Patients with Parkinson’s disease (PD) are numerous and they suffer from hypokinetic dysarthria, which is manifested in all aspects of speech production: respiration, phonation, articulation, nasalization and prosody. This paper provides a review of the methods of the assessment of speech disorders in the context of PD and also discusses the limitations