The goal of this special session is to present original and relevant contributions in the area of information sensing and computing for control of or communication with wearable robots in medical applications.

We investigate speech-coding strategies for brain-machine-interface (BMI) based speech prostheses. We present an articulatory speech-synthesis system using an experimental integrated-circuit vocal tract that models the human vocal tract. Our articulatory silicon vocal tract makes feasible the transmission of low bit-rate speech-coding parameters over a bandwidth-constrained body sensor network (BSN). To the best of our knowledge, this is the first articulatory speech-prosthesis system reported to date. We also present a speech-prosthesis simulator (SPS) as a means to generate realistic articulatory parameter sequences.

In this paper, we present our efforts towards deriving vocal tract shapes from ElectroMagnetic Articulograph data (EMA) via geometric adaptation and matching. We describe a novel approach for adapting Maeda’s geometric model of the vocal tract to one speaker in the MOCHA database. We show how we can rely solely on the EMA data for adaptation. We present our search technique for the vocal tract shapes that best fit the given EMA data. We then describe our approach of synthesizing speech from these shapes. Results on Mel-cepstral distortion reflect improvement in synthesis over the approach we used before without adaptation. Index Terms: MOCHA EMA data, Maeda Model, vocal tract adaptation, articulatory model fitting, articulatory synthesis

Feature computation modules for automatic speech recognition (ASR) systems have long been modeled on the human auditory system. Most current ASR systems model the critical band response and equal loudness characteristics of the auditory system. It has been postulated that more detailed models of the human auditory system can lead to more noise-robust speech recognition. An auditory phenomenon that is of particular relevance to robustness is simultaneous masking, whereby dominant frequencies suppress adjacent weaker frequencies. In this paper, we present a companding-based model that mimics simultaneous masking in the front end of a speech recognizer. In an automotive digits recognition task, the front end improves word error rate by 4.0% (25% relative to Mel cepstra) at -5 dB SNR at the cost of a 1.7% increase at 15 dB SNR.

This work presents a compressing-and-expanding, i.e., companding, strategy for spectral enhancement inspired by the operation of the auditory system. The companding strategy simulates the two-tone suppression phenomena of the auditory system and implements a soft local winner-take-all-like enhancement of the input spectrum. It performs multichannel syllabic compression without degrading spectral contrast. The companding strategy works in an analog fashion without explicit decision making, without the use of the fast Fourier transform, and without any cross-coupling between spectral channels. The strategy is useful in cochlear-implant processors, hearing aids, and speech recognition for enhancing spectral contrast. It is well suited to low-power analog circuit implementations.