Abstract:
Non-intrusive speech-quality assessment uses vocal-tract models, in particular for testing telecommunications systems and equipment. This process requires reduction of the speech stream under assessment into a set of parameters that are sensitive to the types of distortion to be assessed. Once parameterized, the data is used to generate a set of physiologically-based rules for error identification, using a parametric modeling of the shape of the vocal tract itself, by comparison between derived parameters and the output of models of physiologically realistic forms for the vocal tract, and the application of physical constraints on how these can change over time.

Description:
CROSS-REFERENCES TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 10/110,100, filed Apr. 8, 2002, which is a National Phase of International Application No. PCT/GB00/04145, filed Oct. 26, 2000 which designated the U.S., the contents of which are incorporated herein. 
    
    
     BACKGROUND OF THE INVENTION 
     This invention relates to non-intrusive speech-quality assessment using vocal-tract models, in particular for testing telecommunications systems and equipment. 
     Customers are now able to choose a telecommunications service provider based upon price and quality of service. The decision is no longer fixed by monopolies or restricted by limited technology. A range of services is available with differing costs and quality of service. Service providers need the capability to predict customers&#39; perceptions of quality so that networks can be optimized and maintained. Traditionally, networks have been characterized using linear assessment techniques, tone-based signals; and simple engineering metrics, such as signal-to-noise ratio. As networks become more complex, including non-linear elements such as echo cancellers and compressive speech coders, there is a requirement for assessment systems which bear a closer relationship to the human perception of signal quality. This role has typically been filled with expensive and time-consuming subjective tests using human subjects. Such tests are employed for commissioning new network elements, during the design of new coding algorithms, and for testing different network topologies. 
     Recent advances in perceptual modeling have led to the construction of objective auditory models, which can generate predictions of perceived telephony speech quality from a listener&#39;s perspective. These assessment techniques require a known test stimulus to excite a network connection and then use a perceptually-motivated comparison between a reference version of the known test stimulus, and a version of the same stimulus as degraded by the system under test, to provide a measure of the quality of the degraded version as it would be perceived by a human listener. 
       FIG. 1  shows the principle of the BT Laboratories Perceptual Analysis Measurement System (PAMS), disclosed in International Patent Applications W094/00922, W095/01011, and W095/15035. In this system the reference signal  11  comprises a speech-like test stimulus which is used to excite the connection under test  10  to generate a degraded signal  12 . The two signals are then compared in the analysis process  1  to generate an output  18  indicative of the subjective impact of the degradation of the signal  12  when compared with the reference signal  11 . 
     Such assessment techniques are known as “intrusive” because they require the withdrawal of the connection under test  10  from normal service so that it can be excited with a known test stimulus  11 . Removing a connection from normal service renders it unavailable to customers and is expensive to the service provider. In addition, the conditions that generate distortions and errors could be due to network loading levels that are only present at peak times. An out-of-hours assessment could therefore generate artificial quality scores. This means that reliable intrusive testing is relatively expensive in terms of capacity on a customer&#39;s network connection. 
     In general, it would be preferable to continuously monitor the quality of speech at a particular point in the network. In this case, a “non-intrusive” solution is attractive, utilizing the in-service signal to make predictions of quality. Given this information, network traffic can be re-routed through less congested parts of the network if quality drops. A fundamentally different approach is required to analyse a degraded speech signal without a reference signal. The entire process takes place “downstream” of the equipment under test. Non-intrusive techniques are discussed in International Patent Specifications W096/06495 and W096/06496. Current non-intrusive assessment equipment performs measurements such as echo, delay, noise and loudness in an attempt to predict the clarity of a connection. However, a customer&#39;s perception of speech quality is also affected by distortions and irregularities in the speech structure, which are not described by such simple measures. 
     International Patent Specification W097/05730 (now also U.S. Pat. No. 6,035,270) describes a system of this general type which aims to generate an output indicative of how plausible it is that the passing audio stream was generated by the human vocal production system. This is achieved by comparing the audio stream with a spectral model representative of the sounds capable of production by the human vocal system. This process requires pattern recognition to distinguish the spectral characteristics representative of speech and of distortion, so that their presence can be identified. 
     These analysis processes use spectral models, although physiological models  30  have previously been used for speech synthesis—see for example the use of each types of model for these respective purposes in International patent specifications W096/06496 and W097/00432. Unlike a physiological model, spectral models are empirical, and have no intrinsic basis on which to identify what sounds the vocal tract is capable of producing. However, the physiological articulatory models used in the synthesis of continuous speech utilize constraints to ensure the generated speech is smooth and natural sounding. These models would therefore be unsuitable for an assessment process, since in such a process the parameters generated must also be capable of representing “illegal” vocal-tract shapes that the constraints used by such a synthesis model would ordinarily remove. It is the regions that are in error or distorted that contain the information for such an assessment; to remove this at the parameterization stage would make a subsequent analysis of their properties redundant. 
     BRIEF SUMMARY OF THE INVENTION 
     According to exemplary embodiments of the present invention, there is provided a method of identifying distortion in a signal carrying speech, in which the signal is analyzed according to parameters derived from a set of physiologically-based rules using a parametric model of the human vocal tract, to identify parts of the signal which could not have been generated by the human vocal tract. This differs from the prior art systems described above which use empirical spectral analysis rules to distinguish speech from other signals. The analysis process used in the invention instead considers whether physiological combinations exist that could generate a given sound, in order to determine whether that sound should be identified as possible to have been formed by a human vocal tract. 
     Preferably the analysis process comprises the step of reducing a speech stream into a set of parameters that are sensitive to the types of distortion to be assessed. 
     Cavity tracking techniques and context based error spotting may be used to identify signal errors. This allows both instantaneous abnormalities and sequential errors to be identified. Articulatory control parameters (parameters derived from the movement of the individual muscles which control the vocal tract) are extremely useful for speech synthesis applications where their direct relationships with the speech production system can be exploited. However, they are difficult to use for analysis, because the articulatory control parameters are heavily constrained to maintain their conformance to the production of real vocal tract configurations. It is therefore difficult to model error conditions, which necessarily require the modeling of conditions that the vocal tract cannot produce. It is therefore preferred to use acoustic tube models. Such models allow the derivation of vocal-tract descriptors directly from the speech waveform, which is attractive for the present analysis problem, as physiologically unlikely conditions are readily identifiable. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       An embodiment of the invention will now be described, with reference to the accompanying drawings, in which 
         FIG. 1  is a schematic illustration of the PAMS intrusive assessment system already discussed. 
         FIG. 2  is a schematic illustration of the system according to the invention. 
         FIG. 3  illustrates the use of a variable frame length. 
         FIG. 4  is an illustration of the pitch boundaries of a voiced speech event. 
         FIG. 5  illustrates a simplified uniform-cross-sectional-area tube model used in the invention. 
         FIG. 6  is an illustration of the human vocal tract. 
         FIG. 7  illustrates a cavity area sequence. 
     
    
    
     Non-intrusive speech quality assessment processes require parameters with specific properties to be extracted from the speech stream. They should be sensitive to the types of distortions that occur in the network under test; they should be consistent across talkers; and they should not generate ambiguous mappings between speech events and parameters. 
       FIG. 2  shows illustratively the steps carried out by the process of the invention. It will be understood that these may be carried out by software controlling a general-purpose computer. The signal  27  generated by a talker is degraded by the system  28  under test. It is sampled at point  20  and concurrently transmitted to the end user  29 . The parameters and characteristics identified from the process are used to generate an output  26  indicative of the subjective impact of the degradation of the signal  27 , compared with the signal assumed to have been supplied by the talker to the system  28  under test. 
     The degraded signal  27  is first sampled (step  20 ), and several individual processes are then carried out on the sampled signal. 
     DETAILED DESCRIPTION OF THE INVENTION 
     A major problem with non-intrusive speech-quality assessment is lack of information concerning talker characteristics. In the laboratory it is possible to generate talker-specific algorithms with near-perfect error spotting capabilities. These work well because prior knowledge of the talker has been used in development, even though no reference was used. In the real world operation with multiple talkers is necessary, and individual talker variation can generate significant performance reductions. 
     The process of the present invention compensates for this type of error by including talker characteristics in both the parameterization stage and also the assessment phase of the algorithm. The talker characteristics are restricted to those that can be derived from the speech waveform itself, but still yield performance improvements. 
     A model is used in which the overall shape of the human vocal tract is described for each pitch cycle. This approach assumes that the speech to be analyzed is voiced, (i.e. the vocal chords are vibrating, for example vowel sounds) so that the driving stimulus can be assumed to be impulsive. The vocal characteristics of the individual talker of signal  27  are first identified (process  21 ). These are features that are invariant for that talker of signal  27 , such as the average fundamental frequency fo of the voice, which depends on the length of the vocal tract. This process  21  is carried out as follows. It uses a section of speech in the order of 10 seconds to characterize the talker by extracting information about the fundamental frequency and the third formant (third harmonic) values. These values are calculated for the voiced sections of speech only. The mean and standard deviation of the fundamental frequency is used later, during the pitch-cycle identification. The mean of the third formant values is used to estimate the length of the vocal tract. 
     The number of tubes used to calculate vocal tract, measured (as deviations from a notional figure of 17 cm) according to information from the formant positions within the speech waveform. Using the third formant, which is generally present with telephony bandwidth restrictions, it is possible to alter the number of tubes to populate the equivalent lossless tube model. 
     The appropriate number of tube sections is given by the closest integer value to N t , where:
 
 N   t =2 lf   s   /c  
 
where: l=vocal tract length; f s =sample frequency; c=speed of sound: (330 m/sec).
 
     Assuming a sampling frequency of 16 kHz, for the average talker of vocal tract length 17 cm and average 3 rd  formant frequency of 2500 Hz, this leads to sixteen cross-sectional areas being required to populate the tube model. Using a direct proportionality between the average 3 rd  formant frequency for a talker and the length of the vocal tract it is possible to estimate the value of l in the equation above: this estimated value l m  is calculated from:
 
 l   m /17=2500 /d  
 
where d, average 3 rd  formant value.
 
     For a female talker with an average third formant frequency of 3 kHz, this gives an estimated vocal tract length of 14 cm, and the number of tube sections N t  as fourteen. This method for vocal tract length normalization reduces the variation in the parameters extracted from the speech stream so that a general set of error identification rules can be used which are not affected by variations between talker, of which pitch is the main concern. 
     Once characterization has been carried out using the initial ten second section of speech, the parameters identified (mean fundamental frequency, standard deviation, and vocal tract length) may be used for the rest of the speech stream, periodically repeating the initial process in order to detect changes in the talker of signal  27 . 
     The samples taken from the signal  27  (step  20 ) are next used to generate speech parameters from these characteristics. An initial stage of pitch synchronization is carried out (step  22 ). This stage generates a pitch-labeled speech stream, enabling the extraction of parameters from the voiced sections of speech on a variable time base. This allows synchronization with the speech waveform production system, namely the human speech organs, allowing parameters to be derived from whole pitch-periods. This is achieved by selecting the number of samples in each frame such that the frame length corresponds with a cycle of the talker&#39;s speech, as shown in  FIG. 3 . Thus, if the talker&#39;s speech rises and falls in pitch the frame length will track it. This reduces the dependence of the parameterization on gross physical talker properties such as their average fundamental frequency. Note that the actual sampling rate carried out in the sampling step  20  remains constant at 16 kHz—it is the number of such samples going to make up each frame which is varied. 
     Various methods exist for the generation of pitch-synchronous boundaries for parameterization. The present embodiment uses a hybrid temporal spectral method, as described by the inventors in their paper “ Constraint - based pitch - cycle identification using a hybrid temporal spectral method”— 105 th    AES Convention,  1998. This process uses the mean fundamental frequency f 0 , and the standard deviation of this value, to constrain the search for these boundaries. 
     The output of this non-real time method can be seen in  FIG. 4 , which shows the pitch boundaries (marked “X”) for a voiced speech event. It can be seen that these are synchronized with the largest peaks in the voice signal, and thus occur at the same frequency as the fundamental frequency of the talker&#39;s voice. The lengths of the pitch cycles vary to track changes in the pitch of the talker&#39;s voice. 
     Having identified the pitch-synchronous parameters, the parameterization of the vocal tract can now be done (step  23 ). It is important that no constraints are imposed during the parameterization stages that could smooth out or remove signal errors, as they would then not be available for identification in the error identification stage. Articulatory models used in the synthesis of continuous speech utilize constraints to ensure the generated speech is smooth and natural sounding. The parameters generated by a non-intrusive assessment must be capable of representing illegal vocal-tract shapes that would ordinarily be removed by constraints if a synthesis model were used. It is the regions that are in error or distorted that contain the information for such an assessment, to remove this at the parameterization stage would make a subsequent analysis of their properties redundant. 
     In the process of the present embodiment, reflection coefficients are first calculated directly from the speech waveform over the period of a pitch cycle, and these are used to determine the magnitude of each change in cross section area of the vocal tract model, using the number of individual tube elements derived from the talker characteristics already derived (step  21 ). The diameters of the tubes to be used in the model can then be derived from these boundary conditions (step  23 ). An illustration of this representation can be seen in  FIG. 5 , which shows a simplified uniform-cross-sectional-area model of a vocal tract. In this model the vocal tract is modeled as a series of cylindrical tubes having uniform length, and having individual cross sectional areas selected to correspond with the various parts of the vocal tract. The number of such tubes was determined in the preliminary step  21 . 
     For comparison, the true shape of the human vocal tract is illustrated in  FIG. 6 . In the left part of  FIG. 6  there is shown a cross section of a side view of the lower head and throat, with six section lines numbered  1  to  6 . In the right part of  FIG. 6  are shown the views taken on these section lines. The non-circular shape of the real vocal tract, and the fact that the real transitions are not abrupt steps result in higher harmonics being modeled less well in the tube model of  FIG. 5 , but these do not affect the analysis for present purposes. We can therefore use a uniform-cross-sectional-area tube model to describe the instantaneous state of the vocal tract. 
     Certain errors may be apparent from the individual vocal tract parameters themselves, and can be identified directly. However, more generalized error indentification rules may be derived from parameters derived by aggregating these terms. For this reason, dimensionality of the vocal-tract description is reduced even further at this point to maintain a constant number (step  24 ). Methods that track constrictions within the tract yield large variations in the individual cavity parameters during steady-state clean speech attributable to minor differences in the calculation of the constriction point. These differences are significant enough to mask certain errors in degraded speech streams. 
     It has been found experimentally that the best results are produced by splitting the tract into three regions: front cavity, rear cavity, and jaw opening. The accompanying table shows the number of tube elements making up each of the three cavities for each of the numbers of tubes considered. 
     
       
         
               
               
               
               
               
             
           
               
                   
                   
               
               
                   
                 Total 
                   
                   
                   
               
               
                   
                 Number of 
                   
                   
                 Jaw 
               
               
                   
                 Tubes 
                 Rear Cavity 
                 Front Cavity 
                 Opening 
               
               
                   
                   
               
             
             
               
                   
                 12 
                 5 
                 5 
                 2 
               
               
                   
                 13 
                 5 
                 6 
                 2 
               
               
                   
                 14 
                 6 
                 5 
                 3 
               
               
                   
                 15 
                 6 
                 6 
                 3 
               
               
                   
                 16 
                 7 
                 6 
                 3 
               
               
                   
                 17 
                 7 
                 7 
                 3 
               
               
                   
                 18 
                 8 
                 7 
                 3 
               
               
                   
                   
               
             
          
         
       
     
     The total cross sectional area in each of the tube subsets is aggregated to give an indication of cavity opening in each case. 
     Examples of cavity traces can be seen in  FIG. 7 , showing (in the lower part of the figure) the variation in area in each of the three defined cavities during the passage of speech “He was genuinely sorry to see them go”, whose analogue representation is indicated in the part of the Figure. The blank sections correspond to unvoiced sounds and silences, which are not modeled using this system. This is because the cross sectional area parameters can only be calculated during a pitched voice event, such as those which involve glottal excitation caused by vibration of the vocal chords. Under these conditions parameters can be extracted from the speech waveform which describes its state. The rest of the events are unvoiced and are caused by constrictions at different places in the tract causing turbulent airflow, or even a complete closure. The state of the articulators is not so easy to estimate for such events. 
     The cavity sizes extracted (step  24 ) from the vocal tract parameters for each pitch frame are next assessed for physiological violations (step  25 ). Any such violations are taken to be caused by degradation of the signal  27 , and cause an error to be identified. These errors are identified in the output  26 . Errors can be categorized in two major classes, instantaneous and sequential. 
     Instantaneous errors are identified where the size of the cavity value at a given instance in time is assessed as implying a shape that would be impossible for a human vocal tract to take. An extreme example of this is that certain signal distortions can yield excessively large apparent jaw openings—for example 30 cm, and could not have been produced by a human vocal tract. There are other more subtle situations, which have been found empirically, where certain combinations of cavity sizes do not occur in human speech. Any such physiological impossibilities are labeled accordingly, as being indicative of a signal distortion. 
     One of the most common areas of degradation in speech streams in the modern telephony network is through speech coding. Specialized coding schemes, specific to voice signals, can generate distortions when incorrect outputs are generated from the coded parameter stream. In this situation the individual frames may seem entirely appropriate when viewed in isolation, but when the properties of the adjacent frames are taken into account, an error in the degraded signal is apparent. These types of distortion have been termed “sequential errors”. Sequential errors occur quite often in heavily coded speech streams. If incorrect parameters arrive at the decoder, because of miscoding or corruption during transmission, the reconstructed speech stream may contain a spurious speech event. This event may be “legal”—that is, if viewed in isolation or over a short time period it does not require a physiologically impossible instantaneous configuration of the vocal tract—but when heard would be an obvious that an error was present. These types of distortion are identified in the error identification step by assessing the sizes of cavities and vocal tract parameters, in conjunction with the values for preceding and subsequent frames, to identify sequences of cavity sizes which are indicative of signal distortion. 
     The error identification process  25  operates according to predetermined rules arranged to identify individual cavity values, or sequences of such values, which cannot occur physiologically. Some speech events are capable of generation by more than configuration of the vocal tract. This may result in apparent sequential errors when the process responds to a sequence including such an event, if the process selects a vocal tract configuration different from that actually used by the talker. The process is arranged to identify any apparent sequential errors which could result from such ambiguities, so that it can avoid mislabeling them as errors. 
     While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.