Representations of fricatives in subcortical model responses: Comparisons with human consonant perception.

Fricatives are obstruent sound contrasts made by airflow constrictions in the vocal tract that produce turbulence across the constriction or at a site downstream from the constriction. Fricatives exhibit significant intra/intersubject and contextual variability. Yet, fricatives are perceived with high accuracy. The current study investigated modeled neural responses to fricatives in the auditory nerve (AN) and inferior colliculus (IC) with the hypothesis that response profiles across populations of neurons provide robust correlates to consonant perception. Stimuli were 270 intervocalic fricatives (10 speakers × 9 fricatives × 3 utterances). Computational model response profiles had characteristic frequencies that were log-spaced from 125 Hz to 8 or 20 kHz to explore the impact of high-frequency responses. Confusion matrices generated by k-nearest-neighbor subspace classifiers were based on the profiles of average rates across characteristic frequencies as feature vectors. Model confusion matrices were compared with published behavioral data. The modeled AN and IC neural responses provided better predictions of behavioral accuracy than the stimulus spectra, and IC showed better accuracy than AN. Behavioral fricative accuracy was explained by modeled neural response profiles, whereas confusions were only partially explained. Extended frequencies improved accuracy based on the model IC, corroborating the importance of extended high frequencies in speech perception.

Effects of sensorineural hearing loss on formant-frequency discrimination: Measurements and models.

This study concerns the effect of hearing loss on discrimination of formant frequencies in vowels. In the response of the healthy ear to a harmonic sound, auditory-nerve (AN) rate functions fluctuate at the fundamental frequency, F0. Responses of inner-hair-cells (IHCs) tuned near spectral peaks are captured (or dominated) by a single harmonic, resulting in lower fluctuation depths than responses of IHCs tuned between spectral peaks. Therefore, the depth of neural fluctuations (NFs) varies along the tonotopic axis and encodes spectral peaks, including formant frequencies of vowels. This NF code is robust across a wide range of sound levels and in background noise. The NF profile is converted into a rate-place representation in the auditory midbrain, wherein neurons are sensitive to low-frequency fluctuations. The NF code is vulnerable to sensorineural hearing loss (SNHL) because capture depends upon saturation of IHCs, and thus the interaction of cochlear gain with IHC transduction. In this study, formant-frequency discrimination limens (DLFFs) were estimated for listeners with normal hearing or mild to moderate SNHL. The F0 was fixed at 100 Hz, and formant peaks were either aligned with harmonic frequencies or placed between harmonics. Formant peak frequencies were 600 and 2000 Hz, in the range of first and second formants of several vowels. The difficulty of the task was varied by changing formant bandwidth to modulate the contrast in the NF profile. Results were compared to predictions from model auditory-nerve and inferior colliculus (IC) neurons, with listeners' audiograms used to individualize the AN model. Correlations between DLFFs, audiometric thresholds near the formant frequencies, age, and scores on the Quick speech-in-noise test are reported. SNHL had a strong effect on DLFF for the second formant frequency (F2), but relatively small effect on DLFF for the first formant (F1). The IC model appropriately predicted substantial threshold elevations for changes in F2 as a function of SNHL and little effect of SNHL on thresholds for changes in F1.

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.

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.

A causal test of the motor theory of speech perception: a case of impaired speech production and spared speech perception.

The debate about the causal role of the motor system in speech perception has been reignited by demonstrations that motor processes are engaged during the processing of speech sounds. Here, we evaluate which aspects of auditory speech processing are affected, and which are not, in a stroke patient with dysfunction of the speech motor system. We found that the patient showed a normal phonemic categorical boundary when discriminating two non-words that differ by a minimal pair (e.g., ADA-AGA). However, using the same stimuli, the patient was unable to identify or label the non-word stimuli (using a button-press response). A control task showed that he could identify speech sounds by speaker gender, ruling out a general labelling impairment. These data suggest that while the motor system is not causally involved in perception of the speech signal, it may be used when other cues (e.g., meaning, context) are not available.

Anticipatory Deaccenting in Language Comprehension.

We evaluated the hypothesis that listeners can generate expectations about upcoming input using anticipatory deaccenting, in which the absence of a nuclear pitch accent on an utterance-new noun is licensed by the subsequent repetition of that noun (e.g. Drag the SQUARE with the house to the TRIangle with the house). The phonemic restoration paradigm was modified to obscure word-initial segmental information uniquely identifying the final word in a spoken instruction, resulting in a stimulus compatible with two lexical alternatives (e.g. mouse/house). In Experiment 1, we measured participants' final interpretations and response times. Experiment 2 used the same materials in a crowd-sourced gating study. Sentence interpretations at gated intervals, final interpretations, and response times provided converging evidence that the anticipatory deaccenting pattern contributed to listeners' referential expectations. The results illustrate the availability and importance of sentence-level accent patterns in spoken language comprehension.

A gestural account of the velar fricative in Navajo.

Using the framework of Articulatory Phonology, we offer a phonological account of the allophonic variation undergone by the velar fricative phoneme in Navajo, a Southern or Apachean Athabaskan language spoken in Arizona and New Mexico. The Navajo velar fricative strongly coarticulates with the following vowel, varying in both place and manner of articulation. The variation in this velar fricative seems greater than the variation of velars in many well-studied languages. The coronal central fricatives in the inventory, in contrast, are quite phonetically stable. The back fricative of Navajo thus highlights 1) the linguistic use of an extreme form of coarticulation and 2) the mechanism by which languages can control coarticulation. It is argued that the task dynamic model underlying Articulatory Phonology, with the mechanism of gestural blending controlling coarticulation, can account for the multiplicity of linguistically-controlled ways in which velars coarticulate with surrounding vowels without requiring any changes of input specification due to context. The ability of phonological and morphological constraints to restrict the amount of coarticulation argues against strict separation of phonetics and phonology.

Resolving ambiguity: a psycholinguistic approach to understanding prosody processing in high-functioning autism.

Individuals with autism exhibit significant impairments in prosody production, yet there is a paucity of research on prosody comprehension in this population. The current study adapted a psycholinguistic paradigm to examine whether individuals with autism are able to use prosody to resolve syntactically ambiguous sentences. Participants were 21 adolescents with high-functioning autism (HFA), and 22 typically developing controls matched on age, IQ, receptive language, and gender. The HFA group was significantly less likely to use prosody to disambiguate syntax, but scored comparably to controls when syntax alone or both prosody and syntax indicated the correct response. These findings indicate that adolescents with HFA have difficulty using prosody to disambiguate syntax in comparison to typically developing controls, even when matched on chronological age, IQ, and receptive language. The implications of these findings for how individuals with autism process language are discussed.

Effects of prosodically modulated sub-phonetic variation on lexical competition.

Eye movements were monitored as participants followed spoken instructions to manipulate one of four objects pictured on a computer screen. Target words occurred in utterance-medial (e.g., Put the cap next to the square) or utterance-final position (e.g., Now click on the cap). Displays consisted of the target picture (e.g., a cap), a monosyllabic competitor picture (e.g., a cat), a polysyllabic competitor picture (e.g., a captain) and a distractor (e.g., a beaker). The relative proportion of fixations to the two types of competitor pictures changed as a function of the position of the target word in the utterance, demonstrating that lexical competition is modulated by prosodically conditioned phonetic variation.