Technology Research Association of Medical Welfare Apparatus

Nancy Niedzielski

Michael Galler

Hector Raul Javkin

Robert C Boman

The present invention eliminates injection noise in speech produced by esophageal speakers. A speech input signal is digitized. One copy of the digitized signal is used for analysis and the other is passed through a gain switch to an amplifier as output. A Fast Fourier Transform and a mean value of the digitized speech input signal is calculated. The Fast Fourier Transform (FFT) is passed through a morphological filter to produce a filtered spectrum. An occurrence of injection noise is detected by calculating a derivative of the filtered spectrum and determining from the mean value and the derivative a location and value of a largest peak and a second largest peak in the filtered spectrum. If the largest peak is lower in frequency than the second largest peak, and if all points above 2 KHz are less than the mean, then an occurrence of injection noise has been detected. An occurrence of silence is detected by center-clipping the filtered spectrum and determining whether there is any energy within a sliding 10 millisecond window for a predetermined amount of time. If no energy is detected within a sliding 10 millisecond window for a predetermined amount time, then an occurrence of silence has been detected. The output speech signal is passed after the occurrence of injection noise has been detected; and is blocked following an occurrence of silence.

Esophageal speech injection noise detection and rejection

Injection noise and silence are detected in an input speech signal and an external amplifier is switched on or off based on the detected injection noise or silence. The input speech signal is digitized and a first copy of the digitized signal is preemphasized. After the input speech signal is preemphasized, a predetermined number of Mel-frequency cepstral coefficients (MFCCs) and difference cepstra are calculated for each window of the speech signal. A measure of signal energy and a measure of the rate of change of the signal energy is computed. A second copy of the digitized input speech signal is processed using amplitude summation or by differencing a center-clipped signal. The measures of signal energy, rate of change of the signal energy, the Mel coefficients, difference cepstra, and either the amplitude summation value or the differenced value are combined to form an observation vector. Hidden Markov Model (HMM) based decoding is used on the observation vector to detect the occurrence of injection noise or silence. A gain switch on an external speech amplifier is turned on after an occurrence of injection noise and remains on for the duration of speech and the amplifier is turned off when an occurrence of silence is detected.

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Goleta, CA, United States of America

Nancy Niedzielski