Expanding the Human Bandwidth Through Subvocalization and Other Methods

This is a look at human bandwidth and how it applies to human-human interaction and human-computer interaction. The paper discusses what human bandwidth means and what must be done to try to expand it. Current methods of expanding bandwidth are discussed. The methods include detection of subvocal activity, facial expression detection, eye tracking, emotion detection in digital music, pen based musical input systems, and augmented reality. After explaining these methods, the paper focuses on using some of the technologies together to give an idea of what the future of interaction with computers might look like. These proposed ideas include emotion based music, various uses for augmented reality, and composing music with the mind

Silent Speech Recognition by Surface Electromyography

For some time, new methods based on a different than acoustic signal analysis are used for speech recognition. The purpose of nonacoustic signals is to allow silent communication. One of these methods based on the electromyography signal is generated by the human speech articulation system. This article presents a device for electromyographic (EMG) signal acquisition and the first measurements from its use

EARS: Electromyographical Automatic Recognition of Speech

In this paper, we present our research on automatic speech recognition of surface electromyographic signals that are generated by the human articulatory muscles. With parallel recorded audible speech and electromyographic signals, experiments are conducted to show the anticipatory behavior of electromyographic signals with respect to speech signals. Additionally, we demonstrate how to develop phone-based speech recognizers with carefully designed electromyographic feature extraction methods. We show that articulatory feature (AF) classifiers can also benefit from the novel feature, which improve the F-score of the AF classifiers from 0.467 to 0.686. With a stream architecture, the AF classifiers are then integrated into the decoding framework. Overall, the word error rate improves from 86.8 % to 29.9 % on a 100 word vocabulary recognition task.

Electro-myographic patterns of sub-vocal Speech: Records and classification

This paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2.This paper describes the results obtained from recording, processing and classification of words in spoken Spanish by means of analysis of subvocal speech signals. The processed database has six words (forward, backward, right, left, start and stop), In this article, the signals are sensed with surface electrodes (placed on the surface of the throat) and acquired at a sampling frequency of 50 kHz. The signal conditioning consists of a couple of steps, namely the location of area of interest, using energy analysis; and a filtering stage, using Discrete Wavelet Transform. Finally, feature extraction is achieved in the time-frequency domain using Wavelet Packet and statistical techniques for windowing. Classification is carried out with a back propagation neural network whose training is performed with 70% of the database obtained. The correct classification rate was 75%±2

Caracterización del habla sub-vocal mediante electromiografía laríngea

La electromiografía (EMG) es el estudio del comportamiento de las señales eléctricas generadas por los músculos al realizar movimientos; Cuando el ser humano habla, se hace uso de una gran cantidad de músculos asociados intrínseca y extrínsecamente a la laringe, lo cual garantiza que el aparato fonador funcione correctamente y es pueda producir la voz. Para lograr caracterizar el habla sub-vocal del español es necesario realizar electromiografía laríngea no invasiva sobre los movimientos que ejecutan los músculos extrínsecos (Digástrico, Estilohioideo, Milohioideo, Genihioideo, Esternohioideo, Omohioideo, Esternotiroideo, Tirohioideo), que son los encargados de realizar los movimientos verticales y anteroposteriores de la laringe. De esta forma se puede asociar las señales eléctricas adquiridas con la EMG a las vocales pronunciadas por el sujeto de prueba

Towards a silent speech interface for Portuguese: Surface electromyography and the nasality challenge

A Silent Speech Interface (SSI) aims at performing Automatic Speech Recognition (ASR) in the absence of an intelligible acoustic signal. It can be used as a human-computer interaction modality in high-background-noise environments, such as living rooms, or in aiding speech-impaired individuals, increasing in prevalence with ageing. If this interaction modality is made available for users own native language, with adequate performance, and since it does not rely on acoustic information, it will be less susceptible to problems related to environmental noise, privacy, information disclosure and exclusion of speech impaired persons. To contribute to the existence of this promising modality for Portuguese, for which no SSI implementation is known, we are exploring and evaluating the potential of state-of-the-art approaches. One of the major challenges we face in SSI for European Portuguese is recognition of nasality, a core characteristic of this language Phonetics and Phonology. In this paper a silent speech recognition experiment based on Surface Electromyography is presented. Results confirmed recognition problems between minimal pairs of words that only differ on nasality of one of the phones, causing 50% of the total error and evidencing accuracy performance degradation, which correlates well with the exiting knowledge.info:eu-repo/semantics/acceptedVersio

Querying the user properly for high-performance brain-machine interfaces: Recursive estimation, control, and feedback information-theoretic perspectives

We propose a complementary approach to the design of neural prosthetic interfaces that goes beyond the standard approach of estimating desired control signals from neural activity. We exploit the fact that the for a user’s intended application, the dynamics of the prosthetic in fact impact subsequent desired control inputs. We illustrate that changing the dynamic re-sponse of a prosthetic device can make specific tasks signif-icantly easier to accomplish. Our approach relies upon prin-ciples from stochastic control and feedback information the-ory, and we illustrate its effectiveness both theoretically and experimentally- in terms of spelling words from a menu of characters using binary surface electromyography classifica-tion. Index Terms — neural prosthetics, feedback information theory, stochastic control, interface design 1