Surface electromyography (EMG) can be used to record the activation potentials of articulatory muscles while a person speaks. This technique could enable silent speech interfaces, as EMG signals are generated even when people pantomime speech without producing sound. Having effective silent speech interfaces would enable a number of compelling applications, allowing people to communicate in areas where they would not want to be overheard or where the background noise is so prevalent that they could not be heard. In order to use EMG signals in speech interfaces, however, there must be a relatively accurate method to map the signals to speech. Up to this point, it appears that most attempts to use EMG signals for speech interfaces have focused on Automatic Speech Recognition (ASR) based on features derived from EMG signals. Following the lead of other researchers who worked with Electro-Magnetic Articulograph (EMA) data and Non-Audible Murmur (NAM) speech, we explore the alternative idea of using Voice Transformation (VT) techniques to synthesize speech from EMG signals. With speech output, both ASR systems and human listeners can directly use EMG-based systems. We report the results of our preliminary studies, noting the difficulties we encountered and suggesting areas for future work. Index Terms: electromyography, silent speech, voice transformation, speech synthesis

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

In this paper, we present a dynamic articulatory model for phone classification. The model integrates real articulatory information derived from ElectroMagnetic Articulograph (EMA) data into its inner states. It maps from the articulatory space to the acoustic one using an adapted vocal tract model for each speaker and a physiologicallymotivated articulatory synthesis approach. We apply the analysisby-synthesis paradigm in a statistical fashion. We first present a fast approach for deriving analysis-by-synthesis distortion features. Next, the distortion between the speech synthesized from the articulatory states and the incoming speech signal is used to compute the output observation probabilities of the Hidden Markov Model (HMM) used for classification. Experiments with the novel framework show improvements over baseline in phone classification accuracy. Index Terms — Dynamic articulatory modeling, analysis-bysynthesis, articulatory synthesis for recognition, physical model of the vocal tract, hybrid physical and statistical models for classification 1.