Method for identifying speech and call-progression signals

Abstract: A method for real-time recognition of speech and call-progression tone energy includes the steps of digitizing an analog signal present on a signal line (10), correlating (18) N neighboring portions of the digitized signal, performing FFT analysis (20) on the digitized signal, identifying the three largest frequency-domain maxima, determining whether the two largest maxima are above a threshold frequency, and determining whether the ratio of the largest to the third largest maxima exceeds a predetermined value. According to the present invention, the steps of the method may be performed in real time using fixed-point hardware by approximating the correlation and FFT functions.

Description: BACKGROUND OF THE INVENTION 1. Field Of The Invention The present invention relates to digital signal processing and to methods for identifying speech and tone energy on a signal line. More particularly, the present invention relates to methods for identifying and discriminating between tones, such as DTMF tones, dial tones, busy signals and other tones used in telephone systems, and speech energy on a signal line. 2. The Prior Art While numerous theoretical models of speech may be found in the literature, real-time identification of electrical energy as comprising human speech presents a challenge. Real-world, real-time applications of speech-recognition often have low tolerance for false positive and false negative speech-present decisions. An example of such an application is in PBX and other telephone applications, where human speech and call-progression tones are expected to be encountered on the same line within adjacent time windows. There is a need in such systems to accurately identify and distinguish between both call-progression tones and speech components of electrical energy present on a signal line for controlling the operation of the PBX or other control system. The use of false positive and false negative speech-present or tone-present determinations to control the operation of such a system can render a product commercially undesirable. In the ideal case, apparatus capable of reporting the presence of speech or call-progression tones on a signal line would have an accuracy of 100%, however, this goal has not been achievable using prior art technology. DE 31 01 851 A1 discloses a method for sampling speech wherein a zero order autocorrelation coefficient and a partial first order autocorrelation coefficient are derived from a speech signal. If either a first state, wherein the zero order autocorrelation coefficient exceeds a first threshold value, or a second state, wherein the zero order autocorrelation coefficient exceeds a second threshold value and the partial first order autocorrelation coefficient is smaller than a third threshold value are present for a predetermined number of extraction intervals, the beginning of a speech signal has been detected. If a state is detected, which is neither the first state nor the second state, the end of a speech signal is detected. This method is used to discriminate between speech signals and noise signals. US 4,439,639 B1 discloses a digital circuit and a method for the detection of call progress tones in telephone systems. According to this prior art, the PCM signal is linearized and normalized and the level of the linear signal is measured. Then the number of zero level passes are counted. Moreover, the envelope frequency of the signal is detected. The results of the level measurement, the zero level pass counts and the envelope frequency are then evaluated to provide an output signal representing the identity of the input PCM signal. US 3,944,753 B1 discloses an apparatus for distinguishing voice and other noise signals from legitimate multi-frequency tone signals present on telephone or similar communication lines. In this prior art, a frequency drop detector is used to detect data and control signals, voice and other noise signals, as they appear on the line. Upon the occurence of any signal on the line, the frequency drop detector provides a output, which terminates only if the frequency of that signal rapidly drops at a predetermined amount. Depending on the duration of the output signal, the signal on the line is defined as noise or as a legitimate signal. BRIEF DESCRIPTION OF THE INVENTION According to the present invention, a method as claimed in claim 1 for real-time identification of electrical energy on a signal line as human speech or as call-progression tone energy, achieves a higher degree of accuracy than is possible using prior art techniques. According to a presently preferred embodiment of the invention, a method for real-time recognition of speech and call-progression tone energy includes the steps of digitizing an analog signal present on a signal line, correlating N neighboring portions of the digitized signal, performing frequency analysis using fast fourier transform (FFT) analysis on the digitized signal, identifying the three largest frequency-domain maxima, determining whether the two largest maxima are above a threshold frequency, and determining whether the ratio of the largest to the third largest maxima exceeds a predetermined value. According to the present invention, the steps of the method may be performed in real time using fixed-point hardware by approximating the correlation and FFT functions. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a functional block diagram of apparatus for performing the method of the present invention. FIG. 2 is a flow diagram showing the steps of a method according to a presently preferred embodiment of the invention. FIG. 3 is a block diagram illustrating a presently preferred method for sharpening the FFT maxima. DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT Those of ordinary skill in the art will realize that the following description of the present invention is illustrative only and not in any way limiting. Other embodiments of the invention will readily suggest themselves to such skilled persons. Speech may be divided into two components. A first voiced component comprises the English language vowel sounds, and consonants such as "m" "n" etc. A second unvoiced component comprises sounds such as "sh" "th" "p" and "t". While the unvoiced sounds are characterized by little or no correlation, the voiced sounds in speech are highly correlated in the intervals of their pitch. Such a feature recommends the use of correlation techniques as a component of a speech identification method. Certain applications, notably telecommunications, require reliable detection and identification of both speech sounds and tones, such as call progression tones. Because call progression tones, usually unmodulated sinusoidal tones, are ideally perfectly correlated and practically highly correlated, correlation techniques alone cannot be used to discriminate between speech sounds and tones such as call-progression signals on communications lines. According to the present invention, a combination of digital real-time signal processing techniques implemented in fixed-point hardware may be employed to readily differentiate between speech sounds and call progression tones. The method of the present invention provides a reliable and economically viable solution to the speech recognition problem. From the description presented herein, those of ordinary skill in the art will recognize that the method of the present invention could be performed by an appropriately programmed personal computer, such as an IBM compatible computer equipped with a 386 processor. Particular software coded instruction routines for carrying out an actual embodiment of the present invention may be easily provided by those of ordinary skill in the art as a routine exercise. Referring first to FIG. 1, a symbolic representation of apparatus according to the present invention is presented. A signal line 10 to be monitored carries a digitized signal which is to be identified. As those of ordinary skill in the art will readily appreciate, this digitized signal may be in PCM format, sampled at a rate of 8,000 samples/sec. Codec apparatus for creating such signals from analog signals is well known and widely used in the art and consequently need not be disclosed herein. The digitized signal is first processed through low-pass digital filter 12. According to a presently preferred embodiment of the invention, low-pass digital filter 12 may have a linear rolloff characteristic curve in which attenuation begins at 700 Hz and is complete at 800 Hz. Digital filter technology for provision of such a filter is conventional and well understood. The filtered digital signal is then decimated in circuit block 14 using conventional decimation techniques, preferably by taking every fifth sample, resulting in a data sample rate out of block 14 of 1,600 samples/sec. Those of ordinary skill in the art will recognize that the data samples may be placed in a register 16 for holding while they are processed according to the present method. According to a presently preferred embodiment of the invention, the digital signal is then subjected to both correlation (block 18) and FFT analysis (block 20). Both of these forms of digital signal processing are well known to those of ordinary skill in the art and are described in texts such as L. R. Rabiner and R. W. Schafer, Digital Processing of Speech Signals, Prentice Hall, 1978, and R. E. Blahut, Fast Algorithms for Digital Signal Processing, Addison Wesley, 1987. Both of these works are expressly incorporated by reference herein. The results of both the correlation and FFT signal processing steps are evaluated to determine if the energy represented by the digital signal on line 12 comprises speech, tone energy, or noise. A flow diagram representing a method according to a presently preferred embodiment of the invention is depicted in FIG. 2. First, at step 30, correlation is performed on a first block of the data samples. Correlation R xx (k) of two signal samples may be expressed as: According to the present invention, the correlation lag k is chosen to be within the range of human pitch, i.e., between about 2.5 to 20 msec. In addition, the correlation length is chosen to be at least as long as the maximum human pitch, i.e., about 20 msec. At a sample rate of 1,600 samples/sec., the correlation length should be preferably at least 32 samples These correlation parameters may be easily specified in a conventional software routine for performing correlation. Those of ordinary skill in the art will recognize that, in order to be able to perform this digital signal processing economically in real time in a fixed-point hardware environment such as an IBM compatible personal computer, some approximations will need to be employed to simplify the amount of data processing required by the signal processing hardware. According to a presently preferred embodiment of the invention, the maximum R is first determined by employing only the numerator of eq. [1]. At the k value of that maximum R, the function R(k) may be estimated by taking the arithmetic mean rather than the geometric mean specified by the denominator of eq. [1], thus significantly simplifying the amount of processing necessary. Next, at step 32, the correlation result is compared with a predetermined threshold α to determine if there is a possibility that the energy on the line comprises speech. As presently preferred, the threshold may be between about 0.6 and 0.8. To further simplify the signal processing operations, it is preferable to avoid division operations by multiplying α by the arithmetic mean and comparing it with the numerator of eq. [1]. If the correlation result is below the threshold value of α, noise is reported at step 34. There exists the possibility that, even in uncorrelated signals, one random region of a signal will correlate well with a second random region. To avoid false positive results obtained by encountering such glitches, it would be preferable to perform the correlation operation on several consecutive data blocks offset by a time T₀, which may be about 10 to 20 msec If the correlation is below threshold for one or more of the correlation passes, the signal is determined to comprise noise. If the correlation is above threshold for all three passes, there is either speech or tone energy on the line. Steps 36 and 38 illustrate the repetition of the correlation step for N blocks. As previously stated, the presently preferred value for N is 3. As previously noted, the results of the correlation operation indicate the possibility of speech energy, but can also indicate the presence of call-progression tone energy. Therefore, according to the present energy, the digital signal is also subjected to FFT analysis at step 40 to make the final determination regarding whether speech or tone energy is present on the line. It is preferable to perform the FFT analysis with sufficient resolution to identify individual tones. At a sample rate of 1,600 samples/ sec., an FFT length of 128 will produce a resolution of 12.5 Hz, which is satisfactory for the application disclosed herein. FFT analysis of digital signals is well known. Next, at step 42, the FFT analysis results may be "sharpened" according to a presently preferred embodiment of the invention under some conditions. Because of the overlapping band coverage of the FFT process, the frequency response at frequencies between the center bandwidth points defined by the FFT are attenuated and may introduce some ambiguity into the determination. According to this aspect of the invention, the maxima are "sharpened" before the maxima are evaluated. The three highest frequency maxima M₁, M₂, and M₃ are identified at step 44. If the largest or second largest maxima are found to fall below 300 Hz at step 46, speech is identified at step 48 because call progression tones having a frequency of less than 300 Hz are not likely to be employed. If none of the two highest maxima M₁ and M₂, fall below 300 Hz, the largest maxima M₁ is compared with the third highest maxima M₃ at step 50. If the ratio exceeds the threshold, the third maxima is significantly lower energy than the first maxima and the energy is identified as a dual tone at step 52. If the ratio of the first and third maxima is below a preselected threshold (i.e., about 8-10 db) the energy may be identified as speech. Finally, it has been discovered that if a tone is sampled in the time region where it is turned off, there exists the possibility that speech energy can be mistakenly identified. In order to minimize this possibility, the decision to identify speech in an embodiment having a block length of 16 msec can be deferred until three correlations have been reported above threshold and that the FFT analysis has not reported tone energy from the last few, i.e., four, blocks analyzed. This is illustrated at block 54. Referring now to FIG. 3, a block diagram illustrates a presently preferred method for sharpening the FFT maxima. The amplitude of the FFT results in adjacent frequency bands are evaluated in groups of three selected in step 60. The magnitude of each band is compared with its right and left hand neighbors in step 62. If, at step 64, it is determined that the center bandwidth has the largest magnitude and the ratio of the center bandwidth to one of its right or left hand immediate neighbors is greater than 9 db and the ratio to the other immediate neighbor is less than 6 db, then the amplitude of the second highest value is added to the amplitude of the highest value and then that second highest band is zeroed out in step 66. At step 68, it is determined whether all groups of three adjacent maxima have been evaluated. If there are no more groups to evaluate, the process terminates. If additional groups exist, at step 70 the next group is identified and the process returns to step 62. While embodiments and applications of this invention have been shown and described, it would be apparent to those skilled in the art that many more modifications than mentioned above are possible without departing from the inventive concepts herein. The invention, therefore, is not to be restricted except in the scope of the appended claims.

Claims: A method for performing real-time recognition of speech and call-progression tones from electrical energy on a signal line (10) including the steps of: providing a digitized signal representing said energy; correlating (18) N neighboring portions of said digitized signal and reporting "no speech present" if correlation (R) does not exceed a predetermined threshold (α); performing FFT analysis (20) on the digitized signal and identifying first (M1), second (M2) and third (M3) largest frequency-domain maxima; determining whether said first (M1) and second (M2) largest maxima are above a threshold frequency and reporting "speech present" if either said first (M1) and second (M2) largest maxima are below said threshold frequency; and determining whether the ratio of said first (M1) largest to said third (M3) largest maxima exceeds a predetermined value, reporting "tone present" if said ratio is larger than said predetermined value (Th), and reporting "noise present" if said ratio is smaller than said predetermined value (Th). A method according to claim 1, wherein the digitized signal is in PCM format having a sampe rate of 8000 samples/sec. A method according to claim 2, wherein the digitized signal is processed through a low-pass filter (12) having a linear rolloff characteristic curve in which attenation begins at 700 Hz and is complete at 800 Hz. A method according to claim 2 or 3, wherein the digitized signal is decimated (14) by taking every fifth sample to achieve a sample rate of 1600 samples/sec. A method according to claim 1 to 4, wherein the step of correlating N neighboring portions of said digitized signal includes the performance of the following calculation: wherein the correlation lag k is chosen between 2.5 to 20 msec and the correlation length N is chosen to be at least 32 samples. A method according to any of claims 1 to 5, wherein the reporting "no speech present" is performed if correlation does not exceed a predetermined threshold (α) of 0.6 to 0.8. A method according to any of claims 1 to 6, wherein the FFT analysis is performed at a sample rate of 1600 samples/sec at an FFT length of 128. A method according to any of claims 1 to 7, wherein the result of the FFT analysis are sharpened by the following steps: the amplitude of the FFT results in adjacent frequency bands are evaluated in groups of three; for all groups of three adjacent maxima, the following comparison steps are carried out: the magnitude of each frequency band (C) is compared with its right (R) and left (L) neighbors; if it is determined that the center frequency band (C) has the largest magnitude and the ratio of the center frequency band (C) to one of its adjacent left or right (L,R) neighbors is greater than 9 dB and the ratio of the center frequency band (C) to its other adjacent left or right (L,R) neighbor is less than 6 dB, then the amplitude of the second highest value is added to the amplitude of the highest value and the second highest frequency band is zeroed out. A method according to any of claims 1 to 8, wherein said threshold frequency is 300 Hz. A method according to any of claims 1 to 9, wherein said predetermined ratio value is about 8 - 10 dB.

Inventors: EATAMAR DRORY, EATAMAR, DRORY
Applicants: VMX INC, VMX INC.
Application year: 1993
Main classification: H04M1
IPC codes: H04Q1