초록 ▼

The invention relates to a method of identifying a language in which a text is composed in the form of a string of characters, and also to a method of controlling a speech reproduction unit and to a communication device. To be able to carry out language identification with little expenditure, it is

The invention relates to a method of identifying a language in which a text is composed in the form of a string of characters, and also to a method of controlling a speech reproduction unit and to a communication device. To be able to carry out language identification with little expenditure, it is provided according to the invention that a frequency distribution (h1(x), h2(x,y), h3(x,y,z)) of letters in the text is ascertained, the ascertained frequency distribution (h1(x), h2(x,y), h3(x,y,z)) is compared with corresponding frequency distributions (l1(x), l2(x,y), l3(x,y,z)) of available languages, in order to ascertain similarity factors (s1,S2,s3) which indicate the similarity of the language of the text with the available languages, and the language for which the ascertained similarity factor (S1,S2,S3) is the greatest is established as the language of the text.

대표청구항 ▼

The invention relates to a method of identifying a language in which a text is composed in the form of a string of characters, and also to a method of controlling a speech reproduction unit and to a communication device. To be able to carry out language identification with little expenditure, it is

The invention relates to a method of identifying a language in which a text is composed in the form of a string of characters, and also to a method of controlling a speech reproduction unit and to a communication device. To be able to carry out language identification with little expenditure, it is provided according to the invention that a frequency distribution (h1(x), h2(x,y), h3(x,y,z)) of letters in the text is ascertained, the ascertained frequency distribution (h1(x), h2(x,y), h3(x,y,z)) is compared with corresponding frequency distributions (l1(x), l2(x,y), l3(x,y,z)) of available languages, in order to ascertain similarity factors (s1,S2,s3) which indicate the similarity of the language of the text with the available languages, and the language for which the ascertained similarity factor (S1,S2,S3) is the greatest is established as the language of the text. of claim 1: further comprising the step of, constructing an acoustic noise filter over a first set of time frames; and wherein the reducing step is comprised of the steps of, selecting an acoustic noise filter corresponding to the acoustic speech signal and the acoustic noise; and filtering the acoustic noise from the acoustic signal over a second set of time frames using the acoustic noise filter. 5. The method of claim 1 further comprising the steps of: defining time frames within the excitation function and acoustic speech signal based on glottal tissue configuration; and identifying a subset of the time frames where the excitation function is substantially constant; and wherein the reducing step is comprised of the step of averaging amplitudes of the acoustic speech signal over the subset. 6. The method of claim 1: wherein the obtaining an acoustic speech signal step includes the step of capturing the acoustic speech signal with an audio system; and further comprising steps of, identifying positive growing instabilities in the acoustic speech signal; and damping the instabilities by adjusting the audio system. 7. The method of claim 1: wherein the measuring acoustic noise step, includes the step of, detecting an echo signal within the acoustic speech signal corresponding to a voiced-speech portion of the acoustic speech signal; further including the step of, identifying a portion of the acoustic speech signal corresponding to the echo signal; and wherein the reducing step, includes the step of, sign, amplitude, and phase adjusting the portion of the acoustic speech signal to cancel the echo signal. 8. The method of claim 1: wherein the measuring acoustic noise step, includes the step of, measuring background acoustic-noise with a microphone; and wherein the reducing step, includes the step of, sign, amplitude, and phase adjusting the background acoustic-noise to reduce the acoustic noise. 9. The A method for removing acoustic noise from an acoustic speech signal, comprising the steps of: selecting a first set of acoustic speech time frames with timing defined by an excitation function determined using an EM sensor; characterizing qualities of an acoustic noise signal over a second set of time frames with timing defined by an excitation function determined using the EM sensor and by using the acoustic speech signal over said second set of time frames; constructing an acoustic noise filter appropriate to the acoustic speech signal over the first set of time frames and to the characterized noise signal over the second set of time frames; and filtering the acoustic noise signal from the acoustic speech signal over the first set of time frames using the acoustic noise filter, wherein: the characterizing step includes the step of characterizing the qualities of the acoustic noise signal over the first set of time frames; and the constructing step includes the step of constructing the acoustic noise filter using both acoustic speech signal and noise signal information over the first set of time frames, and wherein the constructing step further includes the steps of: selecting a first set of acoustic speech time frames corresponding to a set of voiced speech excitation functions; constructing a speech band-pass filter using spectral information of the voiced speech excitation function obtained using the EM sensor over the first set of time frames; characterizing the acoustic noise over the first set of acoustic speech time frames using the acoustic-signal spectral-information excluded by the speech band-pass filter that is constructed using spectral information of the voiced speech excitation function; constructing the acoustic noise filter over the first set of time frames by using the band-pass filter and the characterized acoustic noise; and filtering the acoustic noise from the acoustic signal over the first set of time frames using the acoustic noise filt er. 10. The A method for removing acoustic noise from an acoustic speech signal, comprising the steps of: selecting a first set of acoustic speech time frames with timing defined by an excitation function determined using an EM sensor; characterizing qualities of an acoustic noise signal over a second set of time frames with timing defined by an excitation function determined using the EM sensor and by using the acoustic speech signal over said second set of time frames; constructing an acoustic noise filter appropriate to the acoustic speech signal over the first set of time frames and to the characterized noise signal over the second set of time frames; and filtering the acoustic noise signal from the acoustic speech signal over the first set of time frames using the acoustic noise filter, partitioning the acoustic speech signal into time frames; calculating an acoustic speech signal energy; calculating an excitation function energy; averaging the acoustic speech signal energy over a subset of the time frames; averaging the excitation function energy over the subset of the time frames; and replacing a portion of the acoustic speech signal in a first time frame with a portion of the acoustic speech signal in a second time frame, if a change in the acoustic speech signal energy in the first time frame exceeds a predetermined threshold, and if the corresponding excitation energy remains constant within predetermined threshold levels. 11. A system for removing acoustic noise from speech, comprising: an EM sensor for generating a speech excitation function from measured movements of a predetermined portion of a vocal tract; an acoustic sensor receiving an acoustic speech signal, corresponding to the speech excitation function from the vocal tract; and a computer for, identifying a first voiced-excitation onset time from the excitation function, subtracting a first predetermined unvoiced time period from the first voiced-excitation onset time to obtain a corresponding first unvoiced-acoustic onset time within the acoustic speech signal; defining a no-speech time period prior to the first unvoiced-acoustic onset time; measuring acoustic noise within the no-speech time period; and reducing the acoustic noise in the acoustic speech signal. system (GIS) to allow rapid capture of field data, asset tracking, and automatic transfer of the data to a GIS database. A pre-defined grammar allows observations to be continuously captured along with GPS location and time, and stored on the field mobile unit. A mobile unit's location is tracked in real time or post processed through wireless RF transmission of location information between the mobile unit and a central processing station. The captured data is electronically transferred to a central processing station for quality assurance and automatic population of the GIS database. The system provides for automatic correlation of field data with other GIS database layers. Tools to generate predefined or user defined reports, work orders, and general data queries allow exploitation of the GIS database. r and LPC parameters during each frame of speech samples; B) detecting said periods of silence, and in response; C) retrieving said gain factor and LPC parameters; D) generating an excitation signal; E) applying said gain factor and said LPC parameters to said excitation signal for generating a frame of said comfort noise; F) playing out said frame of comfort noise; wherein said LPC parameters are estimated by: receiving approximately 20 ms of speech samples prior to said period of silence; performing a windowing operation on said speech samples; computing autocorrelation coefficients of the windows speech samples; applying Levinson-Durbin procedure to estimate LPC coefficients; and averaging the estimated LPC coefficients over successive silence periods to generate said LPC parameters.