Processing speech signal using auditory-like filterbank provides least uncertainty about articulatory gestures

Speech perception difficulty modulates theta-band encoding of articulatory synergies

The human brain tracks available speech acoustics and extrapolates missing information such as the speaker's articulatory patterns. However, the extent to which articulatory reconstruction supports speech perception remains unclear. This study explores the relationship between articulatory reconstruction and task difficulty. Participants listened to sentences and performed a speech-rhyming task. Real kinematic data of the speaker's vocal tract were recorded via electromagnetic articulography (EMA) and aligned to corresponding acoustic outputs. We extracted articulatory synergies from the EMA data with principal component analysis (PCA) and employed partial information decomposition (PID) to separate the electroencephalographic (EEG) encoding of acoustic and articulatory features into unique, redundant, and synergistic atoms of information. We median-split sentences into easy (ES) and hard (HS) based on participants' performance and found that greater task difficulty involved greater encoding of unique articulatory information in the theta band. We conclude that fine-grained articulatory reconstruction plays a complementary role in the encoding of speech acoustics, lending further support to the claim that motor processes support speech perception.NEW & NOTEWORTHY Top-down processes originating from the motor system contribute to speech perception through the reconstruction of the speaker's articulatory movement. This study investigates the role of such articulatory simulation under variable task difficulty. We show that more challenging listening tasks lead to increased encoding of articulatory kinematics in the theta band and suggest that, in such situations, fine-grained articulatory reconstruction complements acoustic encoding.

Nonlinear auditory models yield new insights into representations of vowels

Studies of vowel systems regularly appeal to the need to understand how the auditory system encodes and processes the information in the acoustic signal. The goal of this study is to present computational models to address this need, and to use the models to illustrate responses to vowels at two levels of the auditory pathway. Many of the models previously used to study auditory representations of speech are based on linear filter banks simulating the tuning of the inner ear. These models do not incorporate key nonlinear response properties of the inner ear that influence responses at conversational-speech sound levels. These nonlinear properties shape neural representations in ways that are important for understanding responses in the central nervous system. The model for auditory-nerve (AN) fibers used here incorporates realistic nonlinear properties associated with the basilar membrane, inner hair cells (IHCs), and the IHC-AN synapse. These nonlinearities set up profiles of f0-related fluctuations that vary in amplitude across the population of frequency-tuned AN fibers. Amplitude fluctuations in AN responses are smallest near formant peaks and largest at frequencies between formants. These f0-related fluctuations strongly excite or suppress neurons in the auditory midbrain, the first level of the auditory pathway where tuning for low-frequency fluctuations in sounds occurs. Formant-related amplitude fluctuations provide representations of the vowel spectrum in discharge rates of midbrain neurons. These representations in the midbrain are robust across a wide range of sound levels, including the entire range of conversational-speech levels, and in the presence of realistic background noise levels.

Articulating What Infants Attune to in Native Speech

To become language users, infants must embrace the integrality of speech perception and production. That they do so, and quite rapidly, is implied by the native-language attunement they achieve in each domain by 6-12 months. Yet research has most often addressed one or the other domain, rarely how they interrelate. Moreover, mainstream assumptions that perception relies on acoustic patterns whereas production involves motor patterns entail that the infant would have to translate incommensurable information to grasp the perception-production relationship. We posit the more parsimonious view that both domains depend on commensurate articulatory information. Our proposed framework combines principles of the Perceptual Assimilation Model (PAM) and Articulatory Phonology (AP). According to PAM, infants attune to articulatory information in native speech and detect similarities of nonnative phones to native articulatory patterns. The AP premise that gestures of the speech organs are the basic elements of phonology offers articulatory similarity metrics while satisfying the requirement that phonological information be discrete and contrastive: (a) distinct articulatory organs produce vocal tract constrictions and (b) phonological contrasts recruit different articulators and/or constrictions of a given articulator that differ in degree or location. Various lines of research suggest young children perceive articulatory information, which guides their productions: discrimination of between- versus within-organ contrasts, simulations of attunement to language-specific articulatory distributions, multimodal speech perception, oral/vocal imitation, and perceptual effects of articulator activation or suppression. We conclude that articulatory gesture information serves as the foundation for developmental integrality of speech perception and production.

Advances in real-time magnetic resonance imaging of the vocal tract for speech science and technology research

Real-time magnetic resonance imaging (rtMRI) of the moving vocal tract during running speech production is an important emerging tool for speech production research providing dynamic information of a speaker's upper airway from the entire mid-sagittal plane or any other scan plane of interest. There have been several advances in the development of speech rtMRI and corresponding analysis tools, and their application to domains such as phonetics and phonological theory, articulatory modeling, and speaker characterization. An important recent development has been the open release of a database that includes speech rtMRI data from five male and five female speakers of American English each producing 460 phonetically balanced sentences. The purpose of the present paper is to give an overview and outlook of the advances in rtMRI as a tool for speech research and technology development.

Directly data-derived articulatory gesture-like representations retain discriminatory information about phone categories

How the speech production and perception systems evolved in humans still remains a mystery today. Previous research suggests that human auditory systems are able, and have possibly evolved, to preserve maximal information about the speaker's articulatory gestures. This paper attempts an initial step towards answering the complementary question of whether speakers' articulatory mechanisms have also evolved to produce sounds that can be optimally discriminated by the listener's auditory system. To this end we explicitly model, using computational methods, the extent to which derived representations of "primitive movements" of speech articulation can be used to discriminate between broad phone categories. We extract interpretable spatio-temporal primitive movements as recurring patterns in a data matrix of human speech articulation, i.e. representing the trajectories of vocal tract articulators over time. To this end, we propose a weakly-supervised learning method that attempts to find a part-based representation of the data in terms of recurring basis trajectory units (or primitives) and their corresponding activations over time. For each phone interval, we then derive a feature representation that captures the co-occurrences between the activations of the various bases over different time-lags. We show that this feature, derived entirely from activations of these primitive movements, is able to achieve a greater discrimination relative to using conventional features on an interval-based phone classification task. We discuss the implications of these findings in furthering our understanding of speech signal representations and the links between speech production and perception systems.

Speech Coding in the Brain: Representation of Vowel Formants by Midbrain Neurons Tuned to Sound Fluctuations

Current models for neural coding of vowels are typically based on linear descriptions of the auditory periphery, and fail at high sound levels and in background noise. These models rely on either auditory nerve discharge rates or phase locking to temporal fine structure. However, both discharge rates and phase locking saturate at moderate to high sound levels, and phase locking is degraded in the CNS at middle to high frequencies. The fact that speech intelligibility is robust over a wide range of sound levels is problematic for codes that deteriorate as the sound level increases. Additionally, a successful neural code must function for speech in background noise at levels that are tolerated by listeners. The model presented here resolves these problems, and incorporates several key response properties of the nonlinear auditory periphery, including saturation, synchrony capture, and phase locking to both fine structure and envelope temporal features. The model also includes the properties of the auditory midbrain, where discharge rates are tuned to amplitude fluctuation rates. The nonlinear peripheral response features create contrasts in the amplitudes of low-frequency neural rate fluctuations across the population. These patterns of fluctuations result in a response profile in the midbrain that encodes vowel formants over a wide range of levels and in background noise. The hypothesized code is supported by electrophysiological recordings from the inferior colliculus of awake rabbits. This model provides information for understanding the structure of cross-linguistic vowel spaces, and suggests strategies for automatic formant detection and speech enhancement for listeners with hearing loss.

Time-frequency integration characteristics of hearing are optimized for perception of speech-like acoustic patterns

Several psychoacoustic phenomena such as loudness perception, absolute thresholds of hearing, and perceptual grouping in time are affected by temporal integration of the signal in the auditory system. Similarly, the frequency resolution of the hearing system, often expressed in terms of critical bands, implies signal integration across neighboring frequencies. Although progress has been made in understanding the neurophysiological mechanisms behind these processes, the underlying reasons for the observed integration characteristics have remained poorly understood. The current work proposes that the temporal and spectral integration are a result of a system optimized for pattern detection from ecologically relevant acoustic inputs. This argument is supported by a simulation where the average time-frequency structure of speech that is derived from a large set of speech signals shows a good match to the time-frequency characteristics of the human auditory system. The results also suggest that the observed integration characteristics are learnable from acoustic inputs of the auditory environment using a Hebbian-like learning rule.