Logopenic and nonfluent variants of primary progressive aphasia are differentiated by acoustic measures of speech production

Differentiation of logopenic (lvPPA) and nonfluent/agrammatic (nfvPPA) variants of Primary Progressive Aphasia is important yet remains challenging since it hinges on expert based evaluation of speech and language production. In this study acoustic measures of speech in conjunction with voxel-based morphometry were used to determine the success of the measures as an adjunct to diagnosis and to explore the neural basis of apraxia of speech in nfvPPA. Forty-one patients (21 lvPPA, 20 nfvPPA) were recruited from a consecutive sample with suspected frontotemporal dementia. Patients were diagnosed using the current gold-standard of expert perceptual judgment, based on presence/absence of particular speech features during speaking tasks. Seventeen healthy age-matched adults served as controls. MRI scans were available for 11 control and 37 PPA cases; 23 of the PPA cases underwent amyloid ligand PET imaging. Measures, corresponding to perceptual features of apraxia of speech, were periods of silence during reading and relative vowel duration and intensity in polysyllable word repetition. Discriminant function analyses revealed that a measure of relative vowel duration differentiated nfvPPA cases from both control and lvPPA cases (r2 = 0.47) with 88% agreement with expert judgment of presence of apraxia of speech in nfvPPA cases. VBM analysis showed that relative vowel duration covaried with grey matter intensity in areas critical for speech motor planning and programming: precentral gyrus, supplementary motor area and inferior frontal gyrus bilaterally, only affected in the nfvPPA group. This bilateral involvement of frontal speech networks in nfvPPA potentially affects access to compensatory mechanisms involving right hemisphere homologues. Measures of silences during reading also discriminated the PPA and control groups, but did not increase predictive accuracy. Findings suggest that a measure of relative vowel duration from of a polysyllable word repetition task may be sufficient for detecting most cases of apraxia of speech and distinguishing between nfvPPA and lvPPA

ACCDIST: A Metric for comparing speakers' accents

This paper introduces a new metric for the quantitative assessment of the similarity of speakers' accents. The ACCDIST metric is based on the correlation of inter-segment distance tables across speakers or groups. Basing the metric on segment similarity within a speaker ensures that it is sensitive to the speaker's pronunciation system rather than to his or her voice characteristics. The metric is shown to have an error rate of only 11% on the accent classification of speakers into 14 English regional accents of the British Isles, half the error rate of a metric based on spectral information directly. The metric may also be useful for cluster analysis of accent groups

Relating Objective and Subjective Performance Measures for AAM-based Visual Speech Synthesizers

We compare two approaches for synthesizing visual speech using Active Appearance Models (AAMs): one that utilizes acoustic features as input, and one that utilizes a phonetic transcription as input. Both synthesizers are trained using the same data and the performance is measured using both objective and subjective testing. We investigate the impact of likely sources of error in the synthesized visual speech by introducing typical errors into real visual speech sequences and subjectively measuring the perceived degradation. When only a small region (e.g. a single syllable) of ground-truth visual speech is incorrect we find that the subjective score for the entire sequence is subjectively lower than sequences generated by our synthesizers. This observation motivates further consideration of an often ignored issue, which is to what extent are subjective measures correlated with objective measures of performance? Significantly, we find that the most commonly used objective measures of performance are not necessarily the best indicator of viewer perception of quality. We empirically evaluate alternatives and show that the cost of a dynamic time warp of synthesized visual speech parameters to the respective ground-truth parameters is a better indicator of subjective quality

How to improve TTS systems for emotional expressivity

Several experiments have been carried out that revealed weaknesses of the current Text-To-Speech (TTS) systems in their emotional expressivity. Although some TTS systems allow XML-based representations of prosodic and/or phonetic variables, few publications considered, as a pre-processing stage, the use of intelligent text processing to detect affective information that can be used to tailor the parameters needed for emotional expressivity. This paper describes a technique for an automatic prosodic parameterization based on affective clues. This technique recognizes the affective information conveyed in a text and, accordingly to its emotional connotation, assigns appropriate pitch accents and other prosodic parameters by XML-tagging. This pre-processing assists the TTS system to generate synthesized speech that contains emotional clues. The experimental results are encouraging and suggest the possibility of suitable emotional expressivity in speech synthesis

Speech vocoding for laboratory phonology

Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications