Abstract:
The present invention is a method and system to convert speech signal into a parametric representation in terms of timbre vectors, and to recover the speech signal thereof. The speech signal is first segmented into non-overlapping frames using the glottal closure instant information, each frame is converted into an amplitude spectrum using a Fourier analyzer, and then using Laguerre functions to generate a set of coefficients which constitute a timbre vector. A sequence of timbre vectors can be subject to a variety of manipulations. The new timbre vectors are converted back into voice signals by first transforming into amplitude spectra using Laguerre functions, then generating phase spectra from the amplitude spectra using Kramers-Knonig relations. A Fourier transformer converts the amplitude spectra and phase spectra into elementary waveforms, then superposed to become the output voice. The method and system can be used for voice transformation, speech synthesis, and automatic speech recognition.

Description:
FIELD OF THE INVENTION 
     The present invention generally relates to voice transformation, in particular to voice transformation using orthogonal functions, and its applications in speech synthesis and automatic speech recognition. 
     BACKGROUND OF THE INVENTION 
     Voice transformation involves parameterization of a speech signal into a mathematical format which can be extensively manipulated such that the properties of the original speech, for example, pitch, speed, relative length of phones, prosody, and speaker identity, can be changed, but still sound natural. A straightforward application of voice transformation is singing synthesis. If the new parametric representation is successfully demonstrated to work well in voice transformation, it can be used for speech synthesis and automatic speech recognition. 
     Speech synthesis, or text-to-speech (TTS), involves the use of a computer-based system to convert a written document into audible speech. A good TTS system should generate natural, or human-like, and highly intelligible speech. In the early years, the rule-based TTS systems, or the formant synthesizers, were used. These systems generate intelligible speech, but the speech sounds robotic, and unnatural. 
     Currently, a great majority of commercial TTS systems are concatenative TTS system using the unit-selection method. According to this approach, a very large body of speech is recorded and stored. During the process of synthesis, the input text is first analyzed and the required prosodic features are predicted. Then, appropriate units are selected from a huge speech database, and stitched together. There are always mismatches at the border of consecutive segments from different origins. And there are always cases of required segments that do not exist in the speech database. Therefore, modifications of the recorder speech segments are necessary. Currently, the most popular method of speech modification is the time-domain pitch-synchronized overlap-add method (TD-PSOLA), LPC (linear prediction coefficients), mel-cepstral coefficients and sinusoidal representations. However, using those methods, the quality of voice is severely degraded. To improve the quality of speech synthesis and to allow for the use of a small database, voice transformation is the key. (See Part D of Springer Handbook of Speech Processing, Springer Verlag 2008). 
     Automatic speech recognition (ASR) is the inverse process of speech synthesis. The first step, acoustic processing, reduces the speech signal into a parametric representation. Then, typically using HMM (Hidden Markov Model), with a statistic language model, the most likely text is thus produced. The state-of-the-art parametric representation for speech is LPC (linear prediction coefficients) and mel-cepstral coefficients. Obviously, the accuracy of speech parameterization affects the overall accuracy. (See Part E of Springer Handbook of Speech Processing, Springer Verlag 2008). 
     SUMMARY OF THE INVENTION 
     The present invention is directed to a novel mathematical representation of the human voice as a timbre vector, together with a method of parameterizing speech into a timbre vector, and a method to recover human voice from a series of timbre vectors with variations. According to an exemplary embodiment of the invention, a speech signal is first segmented into non-overlapping frames using the glottal closure moment information. Using Fourier analysis, the speech signal in each frame is converted into amplitude spectrum, then Laguerre functions (based on a set of orthogonal polynomials) are used to convert the amplitude spectrum into a unit vector characteristic to the instantaneous timbre. A timbre vector is formed along with voicedness index, frame duration, and an intensity parameter. Because of the accuracy of the system and method and the complete separation of prosody and timbre, a variety of voice transformation operations can be applied, and the output voice is natural. A straightforward application of voice transformation is singing synthesis. 
     One difference of the current invention from all previous methods is that the frames, or processing units, are non-overlapping, and do not require a window function. All previous parameterization methods, including linear prediction confidents, sinusoidal models, mel-cepstral coefficients and time-domain pitch synchronized overlap add methods rely on overlapping frames requiring a window function (such as Hamming window, Hann window, cosine window, triangular window, Gaussian window, etc.) and a shift time which is smaller than the duration of the frame, which makes an overlap. 
     An important application of the inventive parametric representation is speech synthesis. Using the parametric representation in terms of timbre vectors, the speech segments can be modified to the prosodic requirements and regenerate an output speech with high quality. Furthermore, because of the complete separation of timbre and prosody data, the synthesized speech can have different speaker identity (baby, child, male, female, giant, etc), base pitch (up to three octaves), speed (up to 10 times), and various prosodic variations (calm, emotional, up to shouting). The timbre vector method disclosed in the present invention can be used to build high-quality speech synthesis systems using a compact speech database. 
     Another important application of the inventive parametric representation of speech signal is to serve as the acoustic signal format to improve the accuracy of automatic speech recognition. The timbre vector method disclosed in the present invention can greatly improve the accuracy of automatic speech recognition. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a block diagram of a voice transformation systems using timbre vectors according to an exemplary embodiment of the present invention. 
         FIG. 2  is an explanation of the basic concept of parameterization according to an exemplary embodiment of the present invention. 
         FIG. 3  is the process of segmenting the PCM data according to an exemplary embodiment of the present invention. 
         FIG. 4  is a plot of the Laguerre functions according to an exemplary embodiment of the present invention. 
         FIG. 5  is the data structure of a timbre vector according to an exemplary embodiment of the present invention. 
         FIG. 6  is the binomial interpolation of timbre vectors according to an exemplary embodiment of the present invention. 
         FIG. 7  is a block diagram of a speech synthesis system using timbre vectors according to an exemplary embodiment of the present invention. 
         FIG. 8  is a block diagram of an automatic speech recognition system using timbre vectors according to an exemplary embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     Various exemplary embodiments of the present invention are implemented on a computer system including one or more processors and one or more memory units. In this regard, according to exemplary embodiments, steps of the various methods described herein are performed on one or more computer processors according to instructions encoded on a computer-readable medium. 
       FIG. 1  is a block diagram of the voice transformation system according to an exemplary embodiment of the present invention. The source is the voice from a speaker  101 . Through a microphone  102 , the voice is converted into electrical signal, and recorded in the computer as PCM (Pulse Code Modulation) signal  103 . The PCM signal  103  is then segmented by segmenter  104  into frames  105 , according to segment points  110 . There are two methods to generate the segment points. The first one is to use an electroglottograph (EGG)  106  to detect the glottal closure instants (GCI)  107  directly (See  FIG. 2 ). The second one is to use a glottal closure instants detection unit  108  to generate GCI from the voice waveform. The glottal closure instants (GCI)  107  and the voice signal (PCM)  103  are sent to a processing unit  109 , to generate a complete set of segment points  110 . The details of this process is shown in  FIG. 3 . 
     The voice signal in each frame  105  proceeds through a Fourier analysis unit  111  to generate amplitude spectrum  112 . The amplitude spectrum  112  proceeds through an orthogonal transform unit  113  to generate timbre vectors  114 . In exemplary embodiments, Laguerre functions are the most appropriate mathematical functions for converting the amplitude spectrum into a compact and convenient form (see  FIG. 4 ). Data structure of a timbre vector is shown in  FIG. 5 . 
     After the PCM signal  103  is converted into timbre vectors  114 , a number of voice manipulations can be made according to specifications  115  by voice manipulator  116 , so as to generate new timbre vectors  117 , then the voice can be regenerated using the new timbre vectors  117 . In detail, the steps are as follows: Laguerre transform  118  is used to regenerate amplitude spectrum  119 ; the phase generator  120  (based on Kramers-Kronig relations) is used to generate phase spectrum  121 ; FFT (Fast Fourier Transform)  122  is used to generate an elementary acoustic wave  123 , from the amplitude spectrum and phase spectrum; then those elementary acoustic waves  123  are superposed according to the timing information  124  in the new timbre vectors, each one is delayed by the time of frame duration  125  of the previous frame. The output wave in electric form then drives a loudspeaker  126  to produce an output voice  127 . 
       FIG. 2  shows the process of speech generation, particularly the generation of voiced sections, and the properties of the PCM and EGG signals. Air flow  201  comes from the lungs to the opening between the two vocal cords, or glottis,  202 . If the glottis is constantly open, there is a constant air flow  203 , but no voice signal is generated. At the instant the glottis closes, or a glottal closure occurs, which is always very rapid due to the Bernoulli effect, the inertia of the moving air in the vocal track  204  generates a d&#39;Alembert wave front, then excites an acoustic resonance. The actions of the glottis is monitored by the signals from a electroglottograph (EGG)  205 . When there is a glottal closure, the instrument generates a sharp peak in the derivative of the EGG signal, as shown as  207  in  FIG. 2 . A microphone  206  is placed near the mouth to generate a signal, typically a Pulse Code Modulation signal, or PCM, as shown in  209  in  FIG. 2 . If the glottis remains closed after a closure, as shown as  208 , then the acoustic excitation sustains, as shown as  210 . 
       FIG. 3  shows the details of processing unit  109  to generate the segmentation points. The input data is the PCM signal  301 - 303  and EGG signal  304 , produced by the source speaker  101 . When there are clear peaks in the EGG signal, such as  304 , corresponding to PCM signal  301 , those peaks are selected as the segmentation points  305 . For some quasi-periodic segments of the voice  302 , there is no clear EGG peaks. The segmentation points are generated by comparing the waveform  302  with the neighboring ones  301 , and if the waveform  302  is still periodic, then segmentation points  306  are generated at the same intervals as the segmentation points  305 . If the signal is no longer periodic, such as  303 , the PCM is segmented according to points  307  into frames with an equal interval, here 5 msec. Therefore, the entire PCM signal is segmented into frames. 
     The values of the voice signal at two adjacent closure moments may not match. The following is an algorithm that may be used to match the ends. Let the number of sampling points between two adjacent glottal closures be N, and the original voice signal be x 0 (n). The smoothed signal x(n) in a small interval 0&lt;n&lt;M is defined as 
     
       
         
           
             
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     Where M is about N/10. Otherwise x(n)=x 0 (n). Direct inspection shows that the ends of the waveform are matched, and it is smooth. Therefore, no window functions are required. The waveform in a frame is processed by Fourier analysis to generate an amplitude spectrum. The amplitude spectrum is further processed by a Laguerre transform unit to generate timbre vectors as follows. 
     Laguerre functions are defined as 
     
       
         
           
             
               
                 
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     The amplitude spectrum A(ω) is expended into Laguerre functions 
     
       
         
           
             
               
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     and κ is a scaling factor to maximize accuracy. The norm of the vector C is the intensity parameter I, 
     
       
         
           
             
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     and the normalized Laguerre coefficients are defined as
 
 c   n   =C   n   /I.  
 
     To recover phase spectrum φ(ω) from amplitude spectrum A(ω), Kramers-Kronig relations are used, 
     
       
         
           
             
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     The output wave for a frame, the acoustic exciton, can be calculated from the amplitude spectrum A(ω) and the phase spectrum φ(ω), 
     
       
         
           
             
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       FIG. 4  shows the Laguerre function. After proper scaling, twenty-nine Laguerre functions are used on the frequency scale  401  of 0 to 11 kHz. The first Laguerre function  402  actually probes the first formant. For higher order Laguerre functions, such as the Laguerre function  403 , the resolution in the low-frequency range is successively improved; and extended to the high-frequency range  404 . Because of the accuracy scaling, it makes an accurate but concise representation of the spectrum. 
       FIG. 5  shows the data structure of a timbre vector including the voicedness index (V)  501 , the frame duration (T)  502 , the intensity parameter (I)  503 , and the normalized Laguerre coefficients  504 . 
     There are many possible voice transformation manipulations, including, for example, the following: 
     Timbre interpolation. The unit vector of Laguerre coefficients varies slowly with frames. It can be interpolated for reduced number of frames or extended number of frames for any section of voice to produce natural sounding speech of arbitrary temporal variations. For example, the speech can be made very fast but still recognizable by a blind person. 
     Timbre fusing. By connecting two sets of timbre vectors of two different phonemes and smear-averaging over the juncture, a natural-sounding transition is generated. Phoneme assimilation may be automatically produced. By connecting a syllable ended with [g] with a syllable started with [n], after fusing, the sound [n] is automatically assimilated into [ng]. 
       FIG. 6  shows the principles of the timbre fusing operation. Original timbre vectors from the first phoneme  601  include timbre vectors A, B, and C. Original timbre vectors from the second phoneme  602  include timbre vectors D and E. The output timbre vectors  603  through  607  are weighed averages from the original timbre vectors. For example, output timbre vector D′ is generated from timbre vector C, D, and E using the binomial coefficients 1, 2, and 1; output timbre vector C′ is generated from original timbre vectors A, B, C, D, and E using the binomial coefficients 1, 4, 6, 4, and 1. As a very simple case is shown here, the number of timbre vectors involved can be a larger number of 2 n +1, for example, 9, 17, 33, or 65 for n=3, 4, 5, and 6. 
     Pitch modification. The state-of-the-art technology for pitch modification of speech signal is the time-domain pitch-synchronized overlap-add (TD-PSOLA) method, which can change pitch from −30% to +50%. Otherwise the output would sound unnatural. Here, pitch can be easily modified by changing the time of separation T, then using timbre interpolation to compensate speed. Natural sounding speech can be produced with pitch modifications as large as three octaves. 
     Intensity profiling. Because the intensity parameter I is a property of a frame, it can be changed to produce any stress pattern required by prosody input. 
     Change of speaker identity. First, by rescaling the amplitude spectrum on the frequency axis, the head size can be changed. The voice of an average adult speaker can be changed to that of a baby, a child, a woman, a man, or a giant. Second, by using a filter to alter the spectral envelop, special voice effects can be created. 
     Using those voice manipulation capabilities and timbre fusing (see  FIG. 6 ), high-quality speech synthesizers with a compact database can be constructed using the parametric representation based on timbre vectors (see  FIG. 7 ). The speech synthesis system has two major parts: database building part  101  (the left-hand side of  FIG. 7 ), and the synthesis part  121  (right-hand side of  FIG. 7 ). 
     In the database building unit  701 , a source speaker  702  reads a prepared text. The voice is recorded by a microphone to become the PCM signal  703 . The glottal closure signal is recorded by an electroglottograph (EGG) to become EGG signal  704 . The origin and properties of those signals are shown in  FIG. 2 . The EGG signal and the PCM signal are used by the processing unit  705  to generate a set of segment points  706 . The details of the segmenting process, or the function of the processing unit, is shown in  FIG. 3 . The PCM signal is segmented by the segmenter  707  into frames  708  using the segment points  706 . Each frame is processed by a unit of Fourier analysis  709  to generate amplitude spectrum  710 . The amplitude spectrum of each frame is then processed using a Laguerre transform unit  711  to become a unit vector, representing the instantaneous timbre of that frame, to become the basis of timbre vectors  712 . The Laguerre functions are shown in  FIG. 4 . The structure of the timbre vector is shown in  FIG. 5 . The timbre vectors of various units of speech, such as, for example, phonemes, diphones, demisyllables, syllables, words and even phrases, are then stored in the speech database  720 . 
     In the synthesis unit  721 , the input text  722  together with synthesis parameters  723 , are fed into the frontend  724 . Detailed instructions about the phonemes, intensity and pitch values  725 , for generating the desired speech are generated, then input to a processing unit  726 . The processing unit  726  selects timbre vectors from the database  720 , then converts the selected timbre vectors to a new series of timbre vectors  727  according to the instructions from the process unit  726 , and using timbre fusing if necessary (see  FIG. 6 ). Each timbre vector is converted into an amplitude spectrum  729  by Laguerre transform unit  728 . The phase spectrum  731  is generated from the amplitude spectrum  729  by phase generator  730  using a Kramers-Kronig relations algorithm. The amplitude spectrum  729  and the phase spectrum  731  are sent to a FFT (Fast Fourier Transform) unit  732 , to generate an elementary acoustic wave  733 . Those elementary acoustic waves  733  are than superposed by the superposition unit  735  according to the timing information  734  provided by the new timbre vectors  727 , to generate the final result, output speech signal  736 . 
     The parametric representation of human voice in terms of timbre vectors can also be used as the basis of automatic speech recognition systems. To date, the most widely used acoustic features, or parametric representation of human speech in automatic speech recognition is the mel-cepstrum. First, the speech signal is segmented into frames of fixed length, typically 20 msec, with a window, typically Hann window or Hamming window, and a shift of 10 msec. Those parametric representations are crude and inaccurate. Features that cross the phoneme borders occur very often. 
     The parametric representation based on timbre vectors is more accurate. Especially, a well-behaved timbre distance δ between two frames can be defined as 
     
       
         
           
             
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               = 
               
                 
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                 ⁢ 
                 
                     
                 
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     where c (1)   n  and c (2)   n  are elements of the normalized Laguerre coefficients of the two timbre vectors (see  FIG. 5 ). Experiments have shown that for two timbre vectors of the same phoneme (not diphthong), the distance is less than 0.1. For timbre vectors of different vowels, the distance is 0.1 to 0.6. Furthermore, because of the presence of the voicedness index V (see  FIG. 5 ), vowels and unvoiced consonants are well separated. Because of the intensity parameter I, silence is well separated from real sound. For the recognition of tone languages such as Mandarin, Cantonese, That etc., pitch is an important parameter (see, for example, U.S. Pat. No. 5,751,905 and U.S. Pat. No. 6,510,410). The frame duration T provides a very accurate measure of pitch (see  FIG. 5 ). Therefore, using parametric representation based on timbre vectors, the accuracy of speech recognition can be greatly improved. 
       FIG. 8  shows a block diagram of an automatic speech recognition system based on timbre vectors. The first half of the procedure, converting speech signal into timbre vectors, is similar to step  102  through step  114  of  FIG. 1  for voice transformation. The voice from a speaker  801  is recorded in the computer as PCM signal  803 . The PCM signal  803  is then segmented by segmenter  804  into frames  805 , according to segment points  810 . There are two methods to generate the segment points. The first one is to use an electroglottograph (EGG)  806  to detect the glottal closure instants (GCI)  807  directly (see  FIG. 2 ). The second one is to use the glottal closure instants detection unit  808 , to generate GCI from the voice waveform. The glottal closure instants (GCI)  807  and the voice signal (PCM)  803  are sent to a processing unit  809 , to generate a complete set of segment points  810 . The details of this process are shown in  FIG. 3 . 
     The voice signal in each frame  805  proceeds through a Fourier analysis unit  811  to generate amplitude spectrum  812 . The amplitude spectrum  812  proceeds through a Laguerre transform  813  to generate timbre vectors  814 . 
     The timbre vectors  814  are streamed into acoustic decoder  815 , to compare with the timbre vectors stored in the acoustic models  816 . Possible phoneme sequence  817  is generated. The phoneme sequence is sent to language decoder  818 , assisted with language model  819 , to find the most probable output text  820 . The language decoder  818  may be essentially the same as other automatic speech recognition systems. Because the accuracy of the inventive parametric representation is much higher, the accuracy of the acoustic decoder  815  may be much higher. 
     For using the speech recognition system in a quiet environment, the PCM signals generated through a microphone can be sufficient. In noisy environments, the addition of an electroglottograph  806  can substantially improve the accuracy. 
     In ordinary speech recognition systems, adaptation for a given speaker by recording a good number (for example 100) of spoken sentences from a given speaker and processing it can improve the accuracy. Because of the simplicity of the timbre-vector parametric representation, it is possible to use a single recorded sentence from a given speaker to improve the accuracy. 
     While this invention has been described in conjunction with the exemplary embodiments outlined above, it is evident that many alternatives, modifications and variations will be apparent to those skilled in the art. Accordingly, the exemplary embodiments of the invention, as set forth above, are intended to be illustrative, not limiting. Various changes may be made without departing from the spirit and scope of the invention.