Publications

Abstract

Despite advances in automatic speech recognition (ASR), human input is still essential to produce research-grade segmentations of speech data. Con- ventional approaches to manual segmentation are very labour-intensive. We introduce POnSS, a browser-based system that is specialized for the task of segmenting the onsets and offsets of words, that combines aspects of ASR with limited human input. In developing POnSS, we identified several sub- tasks of segmentation, and implemented each of these as separate interfaces for the annotators to interact with, to streamline their task as much as possible. We evaluated segmentations made with POnSS against a base- line of segmentations of the same data made conventionally in Praat. We observed that POnSS achieved comparable reliability to segmentation us- ing Praat, but required 23% less annotator time investment. Because of its greater efficiency without sacrificing reliability, POnSS represents a distinct methodological advance for the segmentation of speech data.

Abstract

That speakers can vary their speaking rate is evident, but how they accomplish this has hardly been studied. Consider this analogy: When walking, speed can be continuously increased, within limits, but to speed up further, humans must run. Are there multiple qualitatively distinct speech “gaits” that resemble walking and running? Or is control achieved by continuous modulation of a single gait? This study investigates these possibilities through simulations of a new connectionist computational model of the cognitive process of speech production, EPONA, that borrows from Dell, Burger, and Svec’s (1997) model. The model has parameters that can be adjusted to fit the temporal characteristics of speech at different speaking rates. We trained the model on a corpus of disyllabic Dutch words produced at different speaking rates. During training, different clusters of parameter values (regimes) were identified for different speaking rates. In a 1-gait system, the regimes used to achieve fast and slow speech are qualitatively similar, but quantitatively different. In a multiple gait system, there is no linear relationship between the parameter settings associated with each gait, resulting in an abrupt shift in parameter values to move from speaking slowly to speaking fast. After training, the model achieved good fits in all three speaking rates. The parameter settings associated with each speaking rate were not linearly related, suggesting the presence of cognitive gaits. Thus, we provide the first computationally explicit account of the ability to modulate the speech production system to achieve different speaking styles.

Abstract

Purpose: A recent behavioural experiment featuring a noise masking paradigm suggests that Apraxia of Speech (AOS) reflects a disruption of feedforward control, whereas feedback control is spared and plays a more prominent role in achieving and maintaining segmental contrasts. The present study set out to validate the interpretation of AOS as a possible feedforward impairment using computational neural modelling with the DIVA (Directions Into Velocities of Articulators) model.

Method: In a series of computational simulations with the DIVA model featuring a noise-masking paradigm mimicking the behavioural experiment, we investigated the effect of a feedforward, feedback, feedforward + feedback, and an upper motor neuron dysarthria impairment on average vowel spacing and dispersion in the production of six/bVt/speech targets.

Result: The simulation results indicate that the output of the model with the simulated feedforward deficit resembled the group findings for the human speakers with AOS best.

Conclusion: These results provide support to the interpretation of the human observations, corroborating the notion that AOS can be conceptualised as a deficit in feedforward control.

Abstract

Many psycholinguistic models of speech sequence planning make claims about the onset and offset times of planning units, such as words, syllables, and phonemes. These predictions typically go untested, however, since psycholinguists have assumed that the temporal dynamics of the speech signal is a poor index of the temporal dynamics of the underlying speech planning process. This article argues that this problem is tractable, and presents and validates two simple metrics that derive planning unit onset and offset times from the acoustic signal and articulatographic data.