Images of the vocal tract provide the speech researcher with new and unique data. However, quantification of the “biologically important” features present in the image is a significant challenge because these features are typically complex, inherently variable and difficult to describe. The present study quantified cross-sectional tongue shape for vowels in a single plane (post-alveolar) using Principal Component Analysis. A single subject repeated eleven English vowels in two consonant contexts, five times each. Each of the resulting cross-sectional waveshapes (tokens) was represented by 70 samples. The 110 tokens were placed in a 110 × 70 matrix for PCA. The resulting principal components represent a basis set of orthonormal waveshapes with properties particularly suitable for the present project. The first two components accounted for 93% of the variance in the data. The loadings of the eleven vowels on the first two PCs indicated three distinct shape groups. These were examined statistically and found to represent high vowels, front vowels and back vowels. L'imagerie du conduit vocal fournit aux chercheurs en parole des données nouvelles et uniques. Cependant, savoir quantifier l'importance biologique des caractéristiques présentes dans l'image est un enjeu important, car ces caractéristiques sont typiquement complexes, intrinsèquement variables et difficiles à décrire. Ce papier présente l'analyse en composantes principales de la forme de la langue dans le plan coronal (post-alvéolaire) pour les 11 voyelles de l'anglais, répétées 5 fois, par un seul locuteur et dans deux contextes consonantiques. Chacun des contours dans le plan coronal a été représenté par 70 points. Les 110 contours ont été placés dans une matrice 110 × 70 pour l'analyse en composantes principales. Les composantes principales ainsi extraites constituent une base orthonormale dont les propriétés sont particulièrement intéressantes. Les deux premières composantes rendent compte de 93% de la variance mesurée sur les données. Les coefficients de pondération des 11 voyelles selon ces deux premières composantes font apparaître 3 groupes distincts. Ces groupes ont été analysés statistiquement, et se sont révélés représentatifs des voyelles hautes, des voyelles avant et des voyelles arrières.

An analog CMOS integration of a model for the auditory periphery is presented. The model consists of middle ear, basilar membrane, and hair cell/synapse modules which are derived from neurophysiological studies. The circuit realization of each module is discussed, and experimental data of each module's response to sinusoidal excitation are given. The nonlinear speech processing capabilities of the system are demonstrated using the voiced syllable |ba|. The multichannel output of the silicon model corresponds to the time-varying instantaneous firing rates of auditory nerve fibers that have different characteristic frequencies. These outputs are similar to the physiologically obtained responses. The actual implementation uses subthreshold CMOS technology and analog continuous-time circuits, resulting in a real-time, micropower device with potential applications as a preprocessor of auditory stimuli.

It is demonstrated that multiple sources of speech information can be integrated at a subsymbolic level to improve vowel recognition. Feedforward and recurrent neural networks are trained to estimate the acoustic characteristics of a vocal tract from images of the speaker's mouth. These estimates are then combined with the noise-degraded acoustic information, effectively increasing the signal-to-noise ratio and improving the recognition of these noise-degraded signals. Alternative symbolic strategies such as direct categorization of the visual signals into vowels are also presented. The performances of these neural networks compare favorably with human performance and with other pattern-matching and estimation techniques.>

Prelingually, profoundly deaf children have great difficulty achieving intelligible speech. Even after intensive therapy, their speech is deficient in voice pitch, rhythm, stress and intonation, as well as segmental phonetic characteristics. To facilitate the speech training of these children, we are developing two interrelated personal computer (PC) based systems: a school system and a home system. In the school system, speech production is monitored by microphone, electroglottograph, and pneumotachograph. The home system uses only microphone input. Both systems use video displays for providing feedback and reinforcement. The school system allows diagnosis, training by game playing, and specification of games to be played on the home system. The home system provides directed speech practice between therapy sessions.

The present studies are an initial part of a project, the aim of which is development of a cutaneous communication aid for those born profoundly deaf or who become profoundly deaf before acquiring speech and language. A vocoder-type speech analyzer was interfaced to the tactile and visual displays of an Optacon (a reading aid for the blind) so that a time-varying spectral representation of the acoustic signal was displayed. In the production studies, 12 profoundly deaf children, 2-4 years old, were divided into three groups. Progress was assessed in terms of relative frequency of CV production. The group using the visual display and the group using the tactile display showed significant progress. The control group showed no progress. In perception studies adults served as subjects. They were trained and tested on identification of 1) duration, 2) vowels, and 3) spondee words. The tasks were increasingly difficult in the order named. Subjects showed gradual improvement during six weeks of training.

Electric responses to acoustic stimuli have been recorded from the auditory nervous system of cats. Responses of single nerve cells in the cochlear nucleus recorded with microelectrodes, and auditory nerve responses recorded with gross electrodes (which record activity of many cells) are presented as examples of the kinds of data which can be obtained. The behavior of these responses for various changes in stimulus parameters is illustrated together with several methods of data analysis. The data presented are from studies in auditory physiology in which one aim is a description of the neural coding of the temporal pattern of acoustic stimuli.