Mage - Reactive articulatory feature control of HMM-based parametric speech synthesis

In this paper, we present the integration of articulatory control into MAGE, a framework for realtime and interactive (reactive) parametric speech synthesis using hidden Markov models (HMMs). MAGE is based on the speech synthesis engine from HTS and uses acoustic features (spectrum and f0) to model and synthesize speech. In this work, we replace the standard acoustic models with models combining acoustic and articulatory features, such as tongue, lips and jaw positions. We then use feature-space-switched articulatory-to-acoustic regression matrices to enable us to control the spectral acoustic features by manipulating the articulatory features. Combining this synthesis model with MAGE allows us to interactively and intuitively modify phones synthesized in real time, for example transforming one phone into another, by controlling the configuration of the articulators in a visual display. Index Terms: speech synthesis, reactive, articulators 1

Learning to Produce Speech with an Altered Vocal Tract: The Role of Auditory Feedback

Modifying the vocal tract alters a speaker’s previously learned acoustic–articulatory relationship. This study investigated the contribution of auditory feedback to the process of adapting to vocal-tract modifications. Subjects said the word /tɑs/ while wearing a dental prosthesis that extended the length of their maxillary incisor teeth. The prosthesis affected /s/ productions and the subjects were asked to learn to produce ‘‘normal’’ /s/’s. They alternately received normal auditory feedback and noise that masked their natural feedback during productions. Acoustic analysis of the speakers’ /s/ productions showed that the distribution of energy across the spectra moved toward that of normal, unperturbed production with increased experience with the prosthesis. However, the acoustic analysis did not show any significant differences in learning dependent on auditory feedback. By contrast, when naive listeners were asked to rate the quality of the speakers’ utterances, productions made when auditory feedback was available were evaluated to be closer to the subjects’ normal productions than when feedback was masked. The perceptual analysis showed that speakers were able to use auditory information to partially compensate for the vocal-tract modification. Furthermore, utterances produced during the masked conditions also improved over a session, demonstrating that the compensatory articulations were learned and available after auditory feedback was removed

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies

Cortical encoding and decoding models of speech production

To speak is to dynamically orchestrate the movements of the articulators (jaw, tongue, lips, and larynx), which in turn generate speech sounds. It is an amazing mental and motor feat that is controlled by the brain and is fundamental for communication. Technology that could translate brain signals into speech would be transformative for people who are unable to communicate as a result of neurological impairments. This work first investigates how articulator movements that underlie natural speech production are represented in the brain. Building upon this, this work also presents a neural decoder that can synthesize audible speech from brain signals. Data to support these results were from direct cortical recordings of the human sensorimotor cortex while participants spoke natural sentences. Neural activity at individual electrodes encoded a diversity of articulatory kinematic trajectories (AKTs), each revealing coordinated articulator movements towards specific vocal tract shapes. The neural decoder was designed to leverage the kinematic trajectories encoded in the sensorimotor cortex which enhanced performance even with limited data. In closed vocabulary tests, listeners could readily identify and transcribe speech synthesized from cortical activity. These findings advance the clinical viability of using speech neuroprosthetic technology to restore spoken communication

Speech Communication

Contains reports on five research projects.C.J. Lebel FellowshipNational Institutes of Health (Grant 5 T32 NS07040)National Institutes of Health (Grant 5 R01 NS04332)National Science Foundation (Grant 1ST 80-17599)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0254)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0341)U.S. Navy - Naval Electronic Systems Command Contract (N00039-85-C-0290

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

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