Classifications

• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
  • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
  • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
  • G10L25/12—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being prediction coefficients
• • G—PHYSICS
  • G10—MUSICAL INSTRUMENTS; ACOUSTICS
  • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
  • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
  • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
  • G10L25/15—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being formant information

Definitions

• the present invention relates to signal processing and more particularly to signal processing by means of linear prediction coding.
• the invention is primarily concerned with speech signals but is not limited thereto.
• a fundamental problem in the analysis of speech, and in similar fields, concerns the identification of the broad features, or trend, of the power spectrum of a signal in the presence of fine structure brought about by the harmonics of low frequency components and by the presence of noise.
• the broad peaks in the envelope of the power spectrum of a signal are known as formants.
• the problem is a problem of formant identification.
• Present techniques apply either in the frequency domain or in the time domain.
• the power spectrum of the signal is first approximated in some way such as by feeding the signal through a parallel array of filters or by computing the Fourier transform of a segment of the input data. Smoothing procedures are then applied and peaks in the smoothed frequency domain data are taken as estimates of the formant peaks of the spectrum.
• a more sophisticated time domain approach used in the analysis of speech and of which the invention is a special case, is the linear prediction method. Under this method, successive time segments of the signal are each assumed to be the outcome of a stationary autoregressive random process.
• PEF prediction error filter
• ⁇ ' is the transpose of ⁇ .
• Equation (2) is solved for the vector which com prises an estimate of the population prediction error filter (PEF) coefficient vector, ⁇ .
• PPF population prediction error filter
• ⁇ t is the sampling interval
• f N is the Nyquist frequency
• z transform of the PEF coefficients given by
• the linear prediction method of estimating spect trends has several advantages over other methods, viz: (i) only a small number of parameters are required to represent spectral trends, (ii) the estimated spectrum at low orders represents smooth approximation to the population spectrum is completely unaffected by the presence of pitch harmonics, since these cannot affect the values of the covariances, for moderate values of the order, n,
• the invention is an implementation of a multiprocessing approach to real timespectral analysis analogous to that which must occur in living organisms and is intendedto exploit the cheapness and power of contemporary silicon chip technology.
• a linear prediction method is used but it is a method which avoids the time consuming aspects of conventional linear prediction methods. Essentially it involves a process by which the inversion of a single high order matrix is replaced by the simultaneous inversion of a number of second order matrices to yield a solution to equation (2) above. This solution although mathematically imprecise, is sufficiently accurate for practical purposes.
• the spectrum, S n , derived for a particular data sequence is completely contrqlled by the roots of the polynomial Y (z) defined in equation (4).
• the polynomial may be factorized into a number of binomial factors having real coefficients, viz:
• Each binomial fadtor in (7) corresponds to a pair of roots in the complex number plane. Those root pairs which are close to the unit circle are related to peaks in the spectrum, Sn , which are called formants in the case of speech data. Thus some of the binomial coefficient pairs (a 1 , a 2 ) , (b 1 , b 2 ) etc. are associated with particular formants while others which are of less practical importance merely control the spectral trend. Now suppose that the binomial coefficient pair (a 1 , a 2 ) associated with a particular formant is known precisely. They can be used as the coefficients of a non-recursive filter which acts on the data according to equation (32) below to yield a new data sequence whose linear prediction spectrum S* is givenby
• the second order PEF coefficients are found for this second data sequence they will summarize the trend in the spectrum after the partial removal of the dominant peak. Convolution of the original data sequence with this second set of coefficients will have the effect of reducing the residual peaks in this data sequence, leaving the dominant peak more isolated than before and the sequence will yield PEF coefficients less contaminated by the residual peaks. In general, if this process of convoluting a pair of data sequences with the PEF coefficients derived from the alternate sequence is continued, one sequence of sequences will -converge to a limit sequence in which the dominant peak or formant is present in isolation and the other will converge to a limit sequence which has the spectrum of the original sequence but with the dominant peak removed and which can itself be operated on in the same way so as to remove further peaks.
• the data is divided into a plurality of data streams for example, stream A and B, whereby the PEF coefficients computed from stream A are convoluted with the original data stream to yield stream B, while the PEF coefficients from stream B are convoluted with the original stream to yield stream A.
• stream A and B the data is divided into a plurality of data streams for example, stream A and B, whereby the PEF coefficients computed from stream A are convoluted with the original data stream to yield stream B, while the PEF coefficients from stream B are convoluted with the original stream to yield stream A.
• the PEF coefficients yielded by both streams are identical and are a poor approximation to the PEF coefficients of the dominant peak. Some asymmetry must be deliberately introduced into such a network.
• a method of introducing an asymmetry is to filter stream A with a recursive filter (see equation (33) below) whose coefficients are equal to or derived from the PEF coefficients computed from stream A itself.
• This method is effective in isolating the dominant formant in stream A while stream B comprises a stream in which the dominant formant has been removed and which can be further processed in order to isolate remaining formants.
• stream B comprises a stream in which the dominant formant has been removed and which can be further processed in order to isolate remaining formants.
• the network selected to perform a particular task will depend upon the application; the type of spectral information required, the available hardware and so on. Even in the case of speech analysis different networks may be appropriate to each of three distinct applications, viz: phoneme recognition, speaker identification and speech compression for storage and transmission.
• a cascaded two stream embodiment will yield, at any instant, several pairs of coefficients, one pair from each module associated with the formant isolated in the A stream by that module, plus a final coefficient pair associated with the B stream of the final module from which all the formants have been removed.
• Each pair of PEF coefficients, C 1 and C 2 contain information about both the centre frequency and the bandwidth of the corresponding formant (see equation (34) below).
• This second dimension of information is extremely useful in practice since the second coefficient can be used directly as a criterion for accepting or rejecting a particular formant during phoneme recognition; small values of C 2 , that is, less than some threshold value, correspond to peaks which are too broad and weak to be considered valid as formants.
• Fig. 1 shows a circuit block diagram of an embodiment of the device as a formant tracker
• Fig. 2 shows a circuit block diagram of one of the second order prediction filter estimators depicted by circles in Fig. 1,
• Fig. 3 is a graph showing the Fourier transform log power spectrum of a segment of the data sequence fed to the device at line 1.
• Fig. 4 is a graph showing the Fourier transform log power spectrum of a segment of the data sequence fed to the device at line 4, after the first formant has been removed.
• Fig. 5 is a graph showing the Fourier transform log power spectrum of a segment of the data sequence appearing at line 5. The second formant occurs in isolation.
• Fig. 6 is a graph showing the Fourier transform log power spectrum of a segment of the data sequence appearing at line 6. In Fig.
• Peripheral devices such as microphones, analogue filters and clocks are not shown.
• Fig. 1 Inspection of Fig. 1, reveals that it comprises three modules fed respectively by data streams 2, 4 and 6.
• the modules are identical except for the first and are arranged in the form of a hierarchy or cascade, each module except the last passing a data sequence to the next in line.
• the module comprises two non-recursive filters 11 and 12, one recursive filter
• PEF estimator 13 two prediction error filter (PEF) estimators 14 and 15, a coefficient modifier 16 and an output buffer 17.
• the PEF coefficients computed by estimator 15 are passed to the non-recursive filter 11 and the PEF coefficients computed by estimator 14 are passed to non-recursive filter 12, to recursive filter 13, via the coefficient modifier 16 and to the buffer 17.
• the PEF estimator 15 will now compute PEF coefficients related to the residual peaks in data sequence 6 and pass these coefficients to filter 11.
• the residual peaks will then be attenuated in data stream 5 allowing PEF estimator 14 to compute coefficients relating to the dominant peak which are less contaminated by the residual peaks than was previously the case. This effect of isolating the dominant peak will be further reinforced by the action of filter 13.
• coefficient buffer 17, 18, 19 and 20 contain PEF coefficient pairs associated with each of the peaks in the input sequence 1.
• coefficients used in the filters 11, 12 and 13 are initially set to values close to the values they are expected to assume thus allowing more rapid convergence to take place.
• the action of the non-recursive filters constitute a type of negative feedback since they nullify an effect while the recursive filters constitute a form of positive feedback since they exaggerate an effect detected by the PEF estimators. If the filter 13 were not present there would be no asymmetry in the module and data streams 5 and 6 would remain identical.
• the effect of the recursive filter as described above is too strong and although the action of the device in separating formants commences adequately, the device soon ceases to be responsive to changes in the incoming data stream. This effect can be overcome by lessening the degree of "positive feedback" i.e. by modifying the coefficients which are passed to the recursive filters.
• the first formant is not completely removed by the action of a module such as the one described which left behind two small residual peaks not present in the original data spectrum.
• the first formant behaves as a double resonance and the effect is easily overcome by the inclusion of two non-recursive filters (22 & 23) on one side of the first module.
• FIG. 2 A block diagram of one specific embodiment of the second order PEF estimators 14 and 15 referred to above is depicted in Fig. 2. This description is given in digital terms although analogue embodiments are equally feasible.
• the data are presented one at a time by some external device such as a digitizer (not shown) to line 41 in the diagram.
• Delays 42 and 43 and lines 41, 44 and 45 constitute a three word shift register so that at time i ⁇ t the quantities x i , x i-1 and x i- 2 appear at lines 41, 44 and 45 respectively.
• the attenuating factor p will usually be very close to unity and the attenuation of previous .covariance values may best be carried out less frequently than once, every clock cycle, that is, previous values can be multiplied by Np every N clock cycles.
• the PEF coefficients themselves do not change rapidly with time and they too need be computed less frequently than once every clock cycle.
• Fig. 3 shows the input signal on line 1 of Fig. 1 wherein it can be seen that there are four formants F 1 - F 4 present in the spectrum.
• This diagram comprises a 1024 point Fourier transform log power spectrum of the utterance "i" in the word "television”.
• Fig. 4 shows the signal on line 4 of Fig. 1 wherein it can be seen that the first formant F 1 has been removed and the remaining formants are more pronounced.
• FIG. 6 shows the signal on line 6 of Fig. 1 wherein the first two formants have been removed.
• Fig. 5 shows the second formant in isolation, that is, the spectrum of the data stream on line 5 of Fig. 1.
• the coefficient pair summarizing this spectrum appears in buffer 17 of Fig. 1.
• the data sequence comprises a discrete set of quantities, ⁇ x i ⁇ , one definition of theelements, r ij , of the variances and covariances, r ij ,
• ⁇ t is a constant lag or separation of the data in the domain.
• Another simplification is to compute each variance or covariance recursively, viz:
• coefficients may be used as the coefficient of a non-recursive (or "finite impulse response") filter which acts on a data sequence ⁇ x i ⁇ to yield a new data sequence ⁇ y i ⁇ , viz:
• This operation is also referred to as the "convolution" of the data sequence ⁇ x i ⁇ with the PEF coefficients ⁇ 1, C 1 , C 2 ⁇ to yield a new data sequence ⁇ y i ⁇ .
• the PEF coefficients may be used as the coefficients of a recursive (or "infinite impulse response") filter which acts on a data sequence ⁇ u i ⁇ to yield a new data sequence ⁇ V i ⁇ , viz:
• V i - C 1 V i-1 - C 2 V i-2 + u i (33)
• the coefficients C 1 and C 2 summarize the gross features of the power spectrum (i.e. the power spectral density function) of the data sequence from which they were derived.
• the frequency of the peak, f 0 is given by
• ⁇ t is the sampling interval in the discrete case.
• the coefficient C 2 is controlled by the half power width of the peak. It is close to unity when the peak is narrow and is closer to zero when the peak is broad or where more than one peak is present in the spectrum.
• C 2 can be used in some threshold criterion to decide whether a peak is sufficiently narrow to be classified as a single formant and the quantity, f 0 , can be used to determine the frequency of that formant.
• C 1 and C 2 themselves or simple functions of them can be checked against population ranges in order to classify a formant.
• a network for the recognition of different phonemes in speech data could dispense with the coefficient modifiers such as 16 and replace them with switches so that full positive feedback is maintained for a short time causing rapid convergence in the isolation of the formants.
• the recursive filters such as 13 can be switched out of the network for the remainder of the duration of the phoneme.
• Statistically significant changes in the coefficient values appearing in the buffers 17, 18, 19 and 20 can be used to detect the onset of a new phoneme and so cause the recursive filters to again become operative. In this way a speech recognition device can be constructed which is phoneme synchronous, thus avoiding the need for time axis normalization.
• the simplest embodiment of the invention comprises the PEF estimator of Fig. 2.
• This device alone is unsuited to the analysis of spectrally complex signals such as speech but it can be used for the recognition of simpler signals such as the signalling tones used in telephone switching systems. In this way a single device can be used to distinguish between a wide variety of tones when the input (line 1 in Fig. 2) is fed from an appropriate line in the telephone system.
• the coefficients C 1 and C 2 generated by the device are then compared with predefined values in order to classify the incoming signal and to cause the remainder of the telephone system to take appropriate action.
• An analogue embodiment of the device utilizing fixed delays in place of shift registers would be more suited to this application.

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• Engineering & Computer Science (AREA)
• Computational Linguistics (AREA)
• Signal Processing (AREA)
• Health & Medical Sciences (AREA)
• Audiology, Speech & Language Pathology (AREA)
• Human Computer Interaction (AREA)
• Physics & Mathematics (AREA)
• Acoustics & Sound (AREA)
• Multimedia (AREA)
• Compression, Expansion, Code Conversion, And Decoders (AREA)

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Citations (8)

• 1981
  • 1981-05-18 JP JP56501583A patent/JPS57500901A/ja active Pending
  • 1981-05-18 WO PCT/AU1981/000060 patent/WO1981003392A1/en not_active Application Discontinuation
  • 1981-05-18 BR BR8108616A patent/BR8108616A/pt unknown
  • 1981-05-18 EP EP19810901295 patent/EP0052120A4/en not_active Withdrawn
• 1982
  • 1982-01-19 DK DK21282A patent/DK21282A/da not_active Application Discontinuation

Patent Citations (8)

Non-Patent Citations (3)

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