Abstract:
Natural-sounding synthesized speech is obtained from pieced elemental speech units that have their super-class identities known (e.g. phoneme type), and their line spectral frequencies (LSF) set in accordance with a correlation between the desired fundamental frequency and the LSF vectors that are known for different classes in the super-class. The correlation between a fundamental frequency in a class and the corresponding LSF is obtained by, for example, analyzing the database of recorded speech of a person and, more particularly, by analyzing frames of the speech signal.

Description:
RELATED APPLICATIONS  
       [0001]     This is a continuation of U.S. patent application Ser. No. 09/769,112, filed Jan. 25, 2001, which application claims priority from provisional application 60/208,374 filed on May 31, 2000. 
     
    
     BACKGROUND OF THE INVENTION  
       [0002]     This invention relates to speech and, more particularly, to a technique that enables the modification of a speech signal so as to enhance the naturalness of speech sounds generated from the signal.  
         [0003]     Concatenative text-to-speech synthesizers, for example, generate speech by piecing together small units of speech from a recorded-speech database and processing the pieced units to smooth the concatenation boundaries and to match the desired prosodic targets (e.g. speaking speed and pitch contour) accurately. These speech units may be phonemes, half phones, di-phones, etc. One of the more important processing steps that are taken by prior art systems, in order to enhance naturalness of the speech, is modification of pitch (i.e., the fundamental frequency, F 0 ) of the concatenated units, where pitch modification is defined as the altering of F 0 . Typically, the prior art systems do no not modify the magnitude spectrum of the signal. However, it has been observed that large modification factors for F 0  lead to a perceptible decrease in speech quality, and it has been shown that at least one of the reasons for this degradation is the assumption by these prior art system that the magnitude spectrum can remain unaltered. In particular, T. Hirahara has shown in “On the Role of Fundamental Frequency in Vowel Perception,”  The Second Joint Meeting of ASA and ASJ , November 1988, that an increase of F 0  was observed to cause a vowel boundary shift or a vowel height change. Also, in “Vowel F1 as a Function of Speaker Fundamental Frequency,” 110 th    Meeting of JASA , vol. 78, Fall 1985, A. K. Syrdal and S. A. Steele showed that speakers generally increase the first formant as they increase F 0 . These results clearly suggest that the magnitude spectrum must be altered during pitch modification. Recognizing this need, K. Tanaka and M. Abe suggested, in “A New fundamental frequency modification algorithm with transformation of spectrum envelope according to F 0   ,” ICASSP  vol. 2, pp. 951-954, 1997, that the spectrum should be modified by a stretched difference vector of a codebook mapping. A shortcoming of this method is that only three ranges of F 0  (high, middle, and low) are encoded. A smoother evolution of the magnitude spectrum (of an actual speech signal), or the spectrum envelope (of a synthesized speech signal), as a function of changing F 0  is desirable.  
       SUMMARY  
       [0004]     An advance in the art is achieved with an approach where synthesized speech is obtained from pieced elemental speech units that have their super-class identities known (e.g. phoneme type), and their line spectral frequencies (LSF) set in accordance with a correlation between the desired fundamental frequency and the LSF vectors that are known for different classes in the super-class. The correlation between a fundamental frequency in a class and the corresponding LSF is obtained by, for example, analyzing the database of recorded speech of a person and, more particularly, by analyzing frames of the speech signal. In one illustrative embodiment, a text-to-speech synthesis system concatenates frame groupings that belong to specified phonemes, the phonemes are conventionally modified for smooth transitions, the concatenated frames have their prosodic attributes modified to make the synthesized text sound natural—including the fundamental frequency. The spectrum envelop of modified signal is then altered based on the correlation between the modified fundamental frequency in each frame and LSFs. 
     
    
     DETAILED DESCRIPTION  
       [0005]      FIG. 1  presents one illustrative embodiment of a system that benefits from the principles disclosed herein. It is a voice synthesis system; for example, a text-to-speech synthesis system. It includes a controller  10  that accepts text and identifies the sounds (i.e., the speech units) that need to be produced, as well as the prosodic attributes of the sounds; such as pitch, duration and energy of the sounds. The construction of controller  10  is well known to persons skilled in the text-to-speech synthesis art.  
         [0006]     To proceed with the synthesis, controller  10  accesses database  20  that contains the speech units, retrieves the necessary speech units, and applies them to concatenation element  30 , which is a conventional speech synthesis element. Element  30  concatenates the received speech units, making sure that the concatenations are smooth, and applies the result to element  40 . Element  40 , which is also a conventional speech synthesis element, operates on the applied concatenated speech signal to modify the pitch, duration and energy of the speech elements in the concatenated speech signal, resulting in a signal with modified prosodic values.  
         [0007]     It is at this point that the principles disclosed herein come into play, where the focus is on the fact that the pitch is modified. Specifically, the output of element  40  is applied to element  50  that, with the aid of information stored in memory  60 , modifies the magnitude spectrum of the speech signal.  
         [0008]     As indicated above, database  20  contains speech units that are used in the synthesis process. It is useful, however, for database  20  to also contain annotative information that characterizes those speech units, and that information is retrieved concurrently with the associated speech units and applied to elements  30  et seq. as described below. To that end, information about the speech of a selected speaker is recorded during a pre-synthesis process, is subdivided into small speech segments, for example phonemes (which may be on the order of 150 msec), is analyzed, and stored in a relational database table. Illustratively, the table might contain the fields: 
        Record ID,     phoneme label,     average F 0 ,     duration.        
 
         [0013]     To obtain characteristics of the speaker with finer granularity, it is useful to also subdivide the information into frames, for example, 10 msec long, and to store frame information together with frame-annotation information. For example, a second table of database  20  may contain the fields: 
        Record ID,     parent Phoneme record ID,     F o ,     speech samples of the frame.     line spectral frequencies (LSF) vector of the speech samples,     linear prediction coefficients (LPC) vector of the speech samples.        
 
         [0020]     It may be noted that the practitioner has fair latitude as to what specific annotative information is developed for storage in database  20 , and the above fields are merely illustrative. For example the LPC can be computed “on the fly” from the LSFs, but when storage is plentiful, one might wish to store the LPC vectors.  
         [0021]     Once the speech information of the recorded speaker is analyzed and stored in database  20 , in the course of a synthesis process controller  10  can specify to database  20  a particular phoneme type with a particular average pitch and duration, identify a record ID that most closely fulfills the search specification, and then access the second database to obtain the speech samples of all of the frames that correspond to the identified record ID, in the correct sequence. That is, database  20  outputs to element  30  a sequence of speech sample segments. Each segment corresponds to a selected phoneme, and it comprises plurality of frames or, more particularly, it contains the speech samples of the frames that make up the phoneme. It is expected that, as a general proposition, the database will have the desired phoneme type but will not have the precise average F 0  and/or duration that is requested. Element  30  concatenates the phonemes under direction of controller 10 and outputs a train of speech samples that represent the combination of the phonemes retrieved from database  20 , smoothly combined. This train of speech samples is applied to element  40 , where the prosodic values are modified, and in particular where F 0  is modified. The modified signal is applied to element  50 , which modifies the magnitude spectrum of the speech signal in accord with the principles disclosed herein.  
         [0022]     As indicated above, research suggests that the spectral envelope modifications that element  40  needs to perform are related to the changes that are effected in F 0 ; hence, one should expect to find a correlation between the spectral envelope and F 0 . To learn about this correlation, one can investigate different parameters that are related to the spectral envelope, such as the linear predictive codes (LPCs), or the line spectral frequencies (LSFs). We chose to use bark-scale warped LSFs because of their good interpolation and coding properties, as demonstrated by K. K. Paliwal, in “Interpolation Properties of Linear Prediction Parametric Representations,” Proceedings of EUROSPEECH, pp. 1029-32, September 1995. Additionally, the bark-scale warping effects a frequency weighting that is in agreement with human perception.  
         [0023]     In consonance with the decision to use LSFs in seeking a method for estimating the necessary evolution of a spectral envelope with changes to F 0 , we chose to look at the frame records of database  20  and, in particular, at the correlation between the F 0 &#39;s and the LSFs vectors of those records. Through statistical analysis of this information we have determined that, indeed, there are significant correlations between F 0  and LSFs. We have also determined that these correlations are not uniform but, rather, dissimilar even within a set of records that correspond to a given phoneme. Still further, we determined that useful correlation is found when each phoneme is considered to contain Q speech classes.  
         [0024]     In accordance with the principles disclosed herein, therefore, the statistical dependency of F 0  and LSFs is modeled using a Gaussian Mixture Model (GMM), which models the probability distribution of a statistical variable z that is related to both the F 0  and LSFs as the sum of Q multivariate Gaussian functions,  
               p   ⁡     (   z   )       =       ∑     i   =   1     Q     ⁢       α   i     ⁢     N   ⁡     (     z   ,     μ   i     ,     Σ   i       )                   (   1   )             
 
 where N(z, μ i , Σ i ) is a normal distribution with mean vector μ i  and covariance matrix Σ i , α i  is the prior probability of class i, such that  
           ∑     i   =   1     Q     ⁢     α   i       =   1       
 
 and α i ≧0, and z, for example, is [F 0 , LSFs] T . Specifically, employing a conventional Expectation Maximization (EM) algorithm to which the value of Q is applied, as well as the F 0  and LSFs vectors of all frame sub-records in database  20  that correspond to a particular phoneme type, yields the α i , μ i , and Σ i , parameters for the Q classes of that phoneme type. Those parameters, which are developed prior to the synthesis process, for example by processor  51 , are stored in memory  60  under control of processor  51 . 
 
         [0025]     With the information thus developed from the information in database  20 , one can then investigate whether, for a particular phoneme label and a particular F 0 , e.g., F desired , the appropriate corresponding LSF vector, LSF desired , can be estimated with the aid of the statistical information stored in memory  60 .  
         [0026]     More specifically, for a particular speech class, if x={x 1 , x 2 , . . . , x N } is the collection of F 0 &#39;s and y={y 1 , y 2 , . . . , y N } is the corresponding collection of LSF vectors, the question is whether a mapping ℑ can be found that minimizes the mean squared error 
 
ε min   =E└∥y −ℑ( x )∥ 2 ┘  (2) 
 
 where E denotes expectation. To model the joint density, x and y are joined to form  
             z   =     [         x           y         ]             (   3   )             
 
 and the GMM parameters α i , μ i  and Σ i , are estimated as described above in connection with equation (1). 
 
         [0027]     Based on various considerations it was deemed advisable to select the mapping function ℑ to be  
                     ⁢     (   x   )       =     E   ⁡     [     y   ❘   x     ]                   =       ∑     i   =   1     Q     ⁢           h   i     ⁡     (   x   )       ·     [       μ   i   y     +       (     Σ   i   yx     )     ⁢       (     Σ   i   xx     )       -   1       ⁢     (     x   -     μ   i   x       )         ]       ⁢           ⁢   where                     (   4   )                   h   i     =         α   i     ⁢     N   ⁡     (     x   ,     μ   i   x     ,     Σ   i   xx       )             ∑     j   =   1     Q     ⁢       α   j     ⁢     N   ⁡     (     x   ,     μ   j   x     ,     Σ   j   xx       )               ,           (   5   )                   Σ   i     =     [           Σ   i   xx           Σ   i   xy               Σ   i   yx           Σ   i   yy           ]       ,   and           (   6   )                 μ   i     =       [           μ   i   x               μ   i   y           ]     .             (   7   )             
 
         [0028]     From the above, it can be seen that once the α i , μ i  and Σ i , parameters are known for a given phoneme type (from the EM algorithm), equation (6) yields Σ i   xx  Σ i   xy  Σ i   yx  and Σ i   yy , and equation (7) yields μ i   x  and μ i   y . From this information, the parameter h i  is evaluated in accordance with equation (5), allowing a practitioner to estimate the LSF vector, LSF desired , by evaluating ℑ(x), for x=F desired , in accordance with equation (4); i.e., LSF desired ≅ℑ(F desired ).  
         [0029]     In the  FIG. 1  system described above, one input to element  50  is the train of speech samples from element  40  that represent the concatenated speech. This concatenated speech, it may be remembered, was derived from frames of speech samples that database  20  provided. In synchronism with the frames that database  20  outputs, it also outputs the phoneme label that corresponds to the parent phoneme record ID of the frames that are being outputted, as well as the LPC vector coefficients. That is, the speech samples are outputted on line  210 , while the phoneme labels and the LPC coefficients are outputted on line  220 . The phoneme labels track the associated speech sample frames through elements  30  and  40 , and are thus applied to element  50  together with the associated (modified) speech sample frames of the phoneme (or at least with the first frame of the phoneme). The associated LPC coefficients are also applied to element  50  together with the associated (modified) speech sample frames of the phoneme. The speech samples are applied within element  50  to filter  52 , while the phoneme labels and the LPC coefficients are applied within element  50  to processor  51 . Based on the phoneme label, in accord with the principles disclosed above, processor  51  obtains the LSF desired  of that phoneme. To modify the magnitude spectrum for each voiced phoneme frame in this train of samples in accordance with the LSF desired  of that phoneme frame, processor  51  within element  50  develops LPC coefficients that correspond to LSF desired  in accordance with well-known techniques.  
         [0030]     Filter  52  is a digital filter whose coefficients are set by processor  51 . The output of the filter is the spectrum-modified speech signal. We chose a transfer function for filter  52  to be  
                 1   -       ∑     i   =   1     p     ⁢       a   i     ⁢     z     -   i               1   -       ∑     i   =   1     p     ⁢       b   i     ⁢     z     -   i               ,           (   8   )             
 
 where the a i &#39;s are the LPC coefficients applied to element  50  from database  20  (via elements  30  and  40 ), and the b i &#39;s are the LPC coefficients computed within processor  51 . This yields a good result because the magnitude spectrum of the signal at the input to element  50  is approximately equal to the spectrum envelope as represented the LPC vector that is stored in database  20 , that is, the magnitude spectrum is equal to  
         1     1   -       ∑     i   =   1     p     ⁢       a   i     ⁢     z     -   i               ,       
 
 plus some small error. Of course, other transfer functions can also be employed. 
 
         [0031]     Actually, if desired, the speech samples stored in database  20  need not be employed at all in the synthesis process. That is, an arrangement can be employed where speech is coded to yield a sequence of tuples, each of which includes an F 0  value, duration, energy, and phoneme class. This rather small amount of information can then be communicated to a receiver (e.g. in a cellular environment), and the receiver synthesizes the speech. In such a receiver, elements  10 ,  30 , and  40  degenerate into a front end receiver element  15  that. applies a synthesis list of the above-described tuples to element  50 . Based on the desired phoneme and phoneme class, appropriate α i , μ i  and Σ 1  data is retrieved from memory  60 , and based on the desired F 0  the LSF desired  vectors are generated as described above. From the available LSF desired  vectors, LPC coefficients are computed, and a spectrum having the correct envelope is generated from the LPC coefficient. That spectrum is multiplied by sequences of pulses that are created based on the desired F 0 , duration, and energy, yielding the synthesized speech. In other words, a minimal receiver embodiment that employs the principles disclosed herein comprises a memory  60  that stores the information disclosed above, a processor  51  that is responsive to an incoming sequence of list entries, and a spectrum generator element  53  that generates a train of pulses of the required repetition rate (F 0 ) with a spectrum envelope corresponding to  
       1     1   -       ∑     i   =   1     p     ⁢       b   i     ⁢     z     -   i                 
 
 where b i &#39;s are the LPC coefficients computed within processor  51 . 
 
         [0032]     This is illustrated in  FIG. 2 .  
         [0033]     The minimal transmitter embodiment for communicating actual (as contrasted to synthesized) speech comprises a speech analyzer  21  that breaks up an incoming speech signal into phonemes, and frames, and for each frame it develops tuples that specify phoneme type, F 0 , duration, energy, and LSF vectors. The information corresponding to F 0  and the LSF vectors is applied to database  23 , which identifies the phoneme class. That information is combined with the phoneme type, F 0 , duration, and energy information in encoder  22 , and transmitted to the receiver.  
         [0034]     The above-disclosed technique applies to voiced phonemes. When the phonemes are known, as in the above-disclosed example, we call this mode of operation “supervised.” In the supervised mode, we have employed  27  phoneme types in database  20 , and we used a value of 6 for Q. That is, in ascertaining the parameters α i , μ i  and Σ i , the entire collection of frames that corresponded to a particular phoneme type was considered to be divisible into 6 classes.  
         [0035]     At times, the phonemes are not known a priori, or the practitioner has little confidence in the ability to properly divide the recorded speech into known phoneme types. In accordance with the principles disclosed herein, that is not a dispositive failing. We call such mode of operation “unsupervised.” In such mode of operation we scale up the notion of classes. That is, without knowing the phoneme to which frames belong, we assume that the entire set of frames in database  20  forms a universe that can be divided into classes, for example 32 super-classes, or 64 super-classes, where z, for example, is [LSFS] T , and the EM algorithm is applied to the entire set of frames. Each frame is thus assigned to a super-class, and thereafter, each super-class is divided as described above, into Q classes, as described above.  
         [0036]     The above discloses the principles of this invention through, inter alia, descriptions of illustrative embodiments. It should be understood, however, that various other embodiments are possible, and various modifications and improvements are possible without departing from the spirit and scope of this invention. For example, a processor  51  is described that computes the LSF desired  based on a priori computed parameters α i , μ i  and Σ i , pursuant to equations (4)-(7). One can create an embodiment, however, where the LSF desired  vectors can also be computed beforehand, and stored in memory  60 . In such an embodiment, processor  51  needs to only access the memory rather than perform significant computations.