Variability is actively regulated in speech

Although movement variability is often attributed to unwanted noise in the motor system, recent work has demonstrated that variability may be actively controlled. To date, research on regulation of motor variability has relied on relatively simple, laboratory-specific reaching tasks. It is not clear how these results translate to complex, well-practiced and real-world tasks. Here, we test how variability is regulated during speech production, a complex, highly over-practiced and natural motor behavior that relies on auditory and somatosensory feedback. Specifically, in a series of four experiments, we assessed the effects of auditory feedback manipulations that modulate perceived speech variability, shifting every production either towards (inward-pushing) or away from (outward-pushing) the center of the distribution for each vowel. Participants exposed to the inward-pushing perturbation (Experiment 1) increased produced variability while the perturbation was applied as well as after it was removed. Unexpectedly, the outward-pushing perturbation (Experiment 2) also increased produced variability during exposure, but variability returned to near baseline levels when the perturbation was removed. Outward-pushing perturbations failed to reduce participants' produced variability both with larger perturbation magnitude (Experiment 3) or after their variability had increased above baseline levels as a result of the inward-pushing perturbation (Experiment 4). Simulations of the applied perturbations using a state space model of motor behavior suggest that the increases in produced variability in response to the two types of perturbations may arise through distinct mechanisms: an increase in controlled variability in response to the inward-pushing perturbation, and an increase in sensitivity to auditory errors in response to the outward-pushing perturbation. Together, these results suggest that motor variability is actively regulated even in complex and well-practiced behaviors, such as speech.

A New Proposal for Phoneme Acquisition: Computing Speaker-Specific Distribution

Speech is an acoustically variable signal, and one of the sources of this variation is the presence of multiple speakers. Empirical evidence has suggested that adult listeners possess remarkably sensitive (and systematic) abilities to process speech signals, despite speaker variability. It includes not only a sensitivity to speaker-specific variation, but also an ability to utilize speaker variation with other sources of information for further processing. Recently, many studies also showed that young children seem to possess a similar capacity. This suggests continuity in the processing of speaker-dependent speech variability, and suggests that this ability could also be important for infants learning their native language. In the present paper, we review evidence for speaker variability and speech processing in adults, and speaker variability and speech processing in young children, with an emphasis on how they make use of speaker-specific information in word learning situations. Finally, we will build on these findings to make a novel proposal for the use of speaker-specific information processing in phoneme learning in infancy.

Introducción del discurso directo en el latín tardío

Summary:This paper examines verbs of speech used for introducing direct speech in Late Latin and changes which occurred from Classical to Late Latin. It focuses on four verbal forms which were previously identified as the most frequent in selected Late Latin texts, namely inquit, ait, dicens, and dixit. Their properties and patterns of use are examined and their development into quotative markers are considered. It is shown that while in Classical Latin inquit prevails, in Late Latin the range of verbal forms is wider and includes verbal forms that in Classical texts almost never appeared or had different functions than introducing direct speech. It is argued that despite some signs of grammaticalization, none of the examined forms has become a fully developed quotative marker. Thus, concerning the means for introducing direct speech, variability and heterogeneity are the basic characteristics of Late Latin texts.

Speech Assistance for Persons With Speech Impediments Using Artificial Neural Networks

This work focuses on the research related to enabling individuals with speech impairment to use speech-to-text software to recognize and dictate their speech. Automatic Speech Recognition (ASR) tends to be a challenging problem for researchers because of the wide range of speech variability. Some of the variabilities include different accents, pronunciations, speeds, volumes, etc. It is very difficult to train an end-to-end speech recognition model on data with speech impediment due to the lack of large enough datasets, and the difficulty of generalizing a speech disorder pattern on all users with speech impediments. This work highlights the different techniques used in deep learning to achieve ASR and how it can be modified to recognize and dictate speech from individuals with speech impediments.