Patent Publication Number: US-7590529-B2

Title: Method and apparatus for reducing noise corruption from an alternative sensor signal during multi-sensory speech enhancement

Description:
BACKGROUND OF THE INVENTION 
   The present invention relates to noise reduction. In particular, the present invention relates to removing noise from speech signals. 
   A common problem in speech recognition and speech transmission is the corruption of the speech signal by additive noise. In particular, corruption due to the speech of another speaker has proven to be difficult to detect and/or correct. 
   Recently, a system has been developed that attempts to remove noise by using a combination of an alternative sensor, such as a bone conduction microphone, and an air conduction microphone. This system estimates channel responses associated with the transmission of speech and noise through the bone conduction microphone. These channel responses are then used in a direct filtering technique to identify an estimate of the clean speech signal based on a noisy bone conduction microphone signal and a noisy air conduction microphone signal. 
   Although this system works well, it tends to introduce nulls into the speech signal at higher frequencies and also tends to include annoying clicks in the estimated clean speech signal if the user clacks teeth during speech. Thus, a system is needed that improves the direct filtering technique to remove the annoying clicks and improve the clean speech estimate. 
   SUMMARY OF THE INVENTION 
   A method and apparatus classify a portion of an alternative sensor signal as either containing noise or not containing noise. The portions of the alternative sensor signal that are classified as containing noise are not used to estimate a portion of a clean speech signal and the channel response associated with the alternative sensor. The portions of the alternative sensor signal that are classified as not containing noise are used to estimate a portion of a clean speech signal and the channel response associated with the alternative sensor. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of one computing environment in which the present invention may be practiced. 
       FIG. 2  is a block diagram of an alternative computing environment in which the present invention may be practiced. 
       FIG. 3  is a block diagram of a speech enhancement system of the present invention. 
       FIG. 4  is a flow diagram for enhancing speech under one embodiment of the present invention. 
       FIG. 5  is a block diagram of an enhancement model training system of one embodiment of the present invention. 
       FIG. 6  is a flow diagram for enhancing speech under another embodiment of the present invention. 
   

   DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS 
     FIG. 1  illustrates an example of a suitable computing system environment  100  on which the invention may be implemented. The computing system environment  100  is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the invention. Neither should the computing environment  100  be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary operating environment  100 . 
   The invention is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with the invention include, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, telephony systems, distributed computing environments that include any of the above systems or devices, and the like. 
   The invention may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The invention is designed to be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules are located in both local and remote computer storage media including memory storage devices. 
   With reference to  FIG. 1 , an exemplary system for implementing the invention includes a general-purpose computing device in the form of a computer  110 . Components of computer  110  may include, but are not limited to, a processing unit  120 , a system memory  130 , and a system bus  121  that couples various system components including the system memory to the processing unit  120 . The system bus  121  may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus. 
   Computer  110  typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer  110  and includes both volatile and nonvolatile media, removable and non-removable media. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by computer  110 . Communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media. Combinations of any of the above should also be included within the scope of computer readable media. 
   The system memory  130  includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM)  131  and random access memory (RAM)  132 . A basic input/output system  133  (BIOS), containing the basic routines that help to transfer information between elements within computer  110 , such as during start-up, is typically stored in ROM  131 . RAM  132  typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit  120 . By way of example, and not limitation,  FIG. 1  illustrates operating system  134 , application programs  135 , other program modules  136 , and program data  137 . 
   The computer  110  may also include other removable/non-removable volatile/nonvolatile computer storage media. By way of example only,  FIG. 1  illustrates a hard disk drive  141  that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive  151  that reads from or writes to a removable, nonvolatile magnetic disk  152 , and an optical disk drive  155  that reads from or writes to a removable, nonvolatile optical disk  156  such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive  141  is typically connected to the system bus  121  through a non-removable memory interface such as interface  140 , and magnetic disk drive  151  and optical disk drive  155  are typically connected to the system bus  121  by a removable memory interface, such as interface  150 . 
   The drives and their associated computer storage media discussed above and illustrated in  FIG. 1 , provide storage of computer readable instructions, data structures, program modules and other data for the computer  110 . In  FIG. 1 , for example, hard disk drive  141  is illustrated as storing operating system  144 , application programs  145 , other program modules  146 , and program data  147 . Note that these components can either be the same as or different from operating system  134 , application programs  135 , other program modules  136 , and program data  137 . Operating system  144 , application programs  145 , other program modules  146 , and program data  147  are given different numbers here to illustrate that, at a minimum, they are different copies. 
   A user may enter commands and information into the computer  110  through input devices such as a keyboard  162 , a microphone  163 , and a pointing device  161 , such as a mouse, trackball or touch pad. Other input devices (not shown) may include a joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit  120  through a user input interface  160  that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). A monitor  191  or other type of display device is also connected to the system bus  121  via an interface, such as a video interface  190 . In addition to the monitor, computers may also include other peripheral output devices such as speakers  197  and printer  196 , which may be connected through an output peripheral interface  195 . 
   The computer  110  is operated in a networked environment using logical connections to one or more remote computers, such as a remote computer  180 . The remote computer  180  may be a personal computer, a hand-held device, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer  110 . The logical connections depicted in  FIG. 1  include a local area network (LAN)  171  and a wide area network (WAN)  173 , but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet. 
   When used in a LAN networking environment, the computer  110  is connected to the LAN  171  through a network interface or adapter  170 . When used in a WAN networking environment, the computer  110  typically includes a modem  172  or other means for establishing communications over the WAN  173 , such as the Internet. The modem  172 , which may be internal or external, may be connected to the system bus  121  via the user input interface  160 , or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer  110 , or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation,  FIG. 1  illustrates remote application programs  185  as residing on remote computer  180 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used. 
     FIG. 2  is a block diagram of a mobile device  200 , which is an exemplary computing environment. Mobile device  200  includes a microprocessor  202 , memory  204 , input/output (I/O) components  206 , and a communication interface  208  for communicating with remote computers or other mobile devices. In one embodiment, the afore-mentioned components are coupled for communication with one another over a suitable bus  210 . 
   Memory  204  is implemented as non-volatile electronic memory such as random access memory (RAM) with a battery back-up module (not shown) such that information stored in memory  204  is not lost when the general power to mobile device  200  is shut down. A portion of memory  204  is preferably allocated as addressable memory for program execution, while another portion of memory  204  is preferably used for storage, such as to simulate storage on a disk drive. 
   Memory  204  includes an operating system  212 , application programs  214  as well as an object store  216 . During operation, operating system  212  is preferably executed by processor  202  from memory  204 . Operating system  212 , in one preferred embodiment, is a WINDOWS® CE brand operating system commercially available from Microsoft Corporation. Operating system  212  is preferably designed for mobile devices, and implements database features that can be utilized by applications  214  through a set of exposed application programming interfaces and methods. The objects in object store  216  are maintained by applications  214  and operating system  212 , at least partially in response to calls to the exposed application programming interfaces and methods. 
   Communication interface  208  represents numerous devices and technologies that allow mobile device  200  to send and receive information. The devices include wired and wireless modems, satellite receivers and broadcast tuners to name a few. Mobile device  200  can also be directly connected to a computer to exchange data therewith. In such cases, communication interface  208  can be an infrared transceiver or a serial or parallel communication connection, all of which are capable of transmitting streaming information. 
   Input/output components  206  include a variety of input devices such as a touch-sensitive screen, buttons, rollers, and a microphone as well as a variety of output devices including an audio generator, a vibrating device, and a display. The devices listed above are by way of example and need not all be present on mobile device  200 . In addition, other input/output devices may be attached to or found with mobile device  200  within the scope of the present invention. 
     FIG. 3  provides a block diagram of a speech enhancement system for embodiments of the present invention. In  FIG. 3 , a user/speaker  300  generates a speech signal  302  (X) that is detected by an air conduction microphone  304  and an alternative sensor  306 . Examples of alternative sensors include a throat microphone that measures the user&#39;s throat vibrations, a bone conduction sensor that is located on or adjacent to a facial or skull bone of the user (such as the jaw bone) or in the ear of the user and that senses vibrations of the skull and jaw that correspond to speech generated by the user. Air conduction microphone  304  is the type of microphone that is commonly used to convert audio air-waves into electrical signals. 
   Air conduction microphone  304  also receives ambient noise  308  (V) generated by one or more noise sources  310 . Depending on the type of alternative sensor and the level of the noise, noise  308  may also be detected by alternative sensor  306 . However, under embodiments of the present invention, alternative sensor  306  is typically less sensitive to ambient noise than air conduction microphone  304 . Thus, the alternative sensor signal generated by alternative sensor  306  generally includes less noise than air conduction microphone signal generated by air conduction microphone  304 . Although alternative sensor  306  is less sensitive to ambient noise, it does generate some sensor noise  320  (W). 
   The path from speaker  300  to alternative sensor signal  316  can be modeled as a channel having a channel response H. The path from ambient noise sources  310  to alternative sensor signal  316  can be modeled as a channel having a channel response G. 
   The alternative sensor signal from alternative sensor  306  and the air conduction microphone signal from air conduction microphone  304  are provided to analog-to-digital converters  322  and  324 , respectively, to generate a sequence of digital values, which are grouped into frames of values by frame constructors  326  and  328 , respectively. In one embodiment, A-to-D converters  322  and  324  sample the analog signals at 16 kHz and 16 bits per sample, thereby creating 32 kilobytes of speech data per second and frame constructors  326  and  328  create a new respective frame every 10 milliseconds that includes 20 milliseconds worth of data. 
   Each respective frame of data provided by frame constructors  326  and  328  is converted into the frequency domain using Fast Fourier Transforms (FFT)  330  and  332 , respectively. This results in frequency domain values  334  (B) for the alternative sensor signal and frequency domain values  336  (Y) for the air conduction microphone signal. 
   The frequency domain values for the alternative sensor signal  334  and the air conduction microphone signal  336  are provided to enhancement model trainer  338  and direct filtering enhancement unit  340 . Enhancement model trainer  338  trains model parameters that describe the channel responses H and G as well as ambient noise V and sensor noise W based on alternative sensor values B and air conduction microphone values Y. These model parameters are provided to direct filtering enhancement unit  340 , which uses the parameters and the frequency domain values B and Y to estimate clean speech signal  342  ({circumflex over (X)}). 
   Clean speech estimate  342  is a set of frequency domain values. These values are converted to the time domain using an Inverse Fast Fourier Transform  344 . Each frame of time domain values is overlapped and added with its neighboring frames by an overlap-and-add unit  346 . This produces a continuous set of time domain values that are provided to a speech process  348 , which may include speech coding or speech recognition. 
   The present inventors have found that the system for identifying clean signal estimates shown in  FIG. 3  can be adversely affected by transient noise, such as teeth clack, that is detected more by alternative sensor  306  than by air conduction microphone  304 . The present inventors have found that such transient noise corrupts the estimate of the channel response H, causing nulls in the clean signal estimates. In addition, when an alternative sensor value B is corrupted by such transient noise, it causes the clean speech value that is estimated from that alternative sensor value to also be corrupted. 
   The present invention provides direct filtering techniques for estimating clean speech signal  342  that avoids corruption of the clean speech estimate caused by transient noise in the alternative sensor signal such as teeth clack. In the discussion below, this transient noise is referred to as teeth clack to avoid confusion with other types of noise found in the system. However, those skilled in the art will recognize that the present invention may be used to identify clean signal values when the system is affected by any type of noise that is detected more by the alternative sensor than by the air conduction microphone. 
     FIG. 4  provides a flow diagram of a batch update technique used to estimate clean speech values from noisy speech signals using techniques of the present invention. 
   In step  400 , air conduction microphone values (Y) and alternative sensor values (B) are collected. These values are provided to enhancement model trainer  338 . 
     FIG. 5  provides a block diagram of trainer  338 . Within trainer  338 , alternative sensor values (B) and air conduction microphone values (Y) are provided to a speech detection unit  500 . 
   Speech detection unit  500  determines which alternative sensor values and air conduction microphone values correspond to the user speaking and which values correspond to background noise, including background speech, at step  402 . 
   Under one embodiment, speech detection unit  500  determines if a value corresponds to the user speaking by identifying low energy portions of the alternative sensor signal, since the energy of the alternative sensor noise is much smaller than the speech signal captured by the alternative sensor signal. 
   Specifically, speech detection unit  500  identifies the energy of the alternative sensor signal for each frame as represented by each alternative sensor value. Speech detection unit  500  then searches the sequence of frame energy values to find a peak in the energy. It then searches for a valley after the peak. The energy of this valley is referred to as an energy separator, d. To determine if a frame contains speech, the ratio, k, of the energy of the frame, e, over the energy separator, d, is then determined as: k=e/d. A speech confidence, q, for the frame is then determined as: 
                 q   =     {           0   :           k   &lt;   1                   k   -   1       α   -   1       :           1   ≤   k   ≤   α               1   :           k   &gt;   α                     EQ   .           ⁢   1               
where α defines the transition between two states and in one implementation is set to 2. Finally, the average confidence value of the 5 neighboring frames (including itself) is used as the final confidence value for the frame.
 
   Under one embodiment, a fixed threshold value is used to determine if speech is present such that if the confidence value exceeds the threshold, the frame is considered to contain speech and if the confidence value does not exceed the threshold, the frame is considered to contain non-speech. Under one embodiment, a threshold value of 0.1 is used. 
   In other embodiments, known speech detection techniques may be applied to the air conduction speech signal to identify when the speaker is speaking. Typically, such systems use pitch trackers to identify speech frames, since such frames usually contain harmonics that are not present in non-speech. 
   Alternative sensor values and air conduction microphone values that are associated with speech are stored as speech frames  504  and values that are associated with non-speech are stored as non-speech frames  502 . 
   Using the values in non-speech frames  502 , a background noise estimator  506 , an alternative sensor noise estimator  508  and a channel response estimator  510 , estimate model parameters that describe the background noise, the alternative sensor noise, and the channel response G, respectively, at step  404 . 
   Under one embodiment, the real and imaginary parts of the background noise, V, and the real and imaginary parts of the sensor noise, W, are modeled as independent zero-mean Gaussians such that:
 
 V=N ( O,σ   v   2 )  Eq. 2
 
 W=N ( O,σ   w   2 )  Eq. 3
 
where σ v   2  is the variance for background noise V and σ w   2  is the variance for sensor noise W.
 
   The variance for the background noise, σ v   2 , is estimated from values of the air conduction microphone during the non-speech frames. Specifically, the air conduction microphone values Y during non-speech are assumed to be equal to the background noise, V. Thus, the values of the air conduction microphone Y can be used to determine the variance σ v   2 , assuming that the values of Y are modeled as a zero mean Gaussian during non-speech. Under one embodiment, this variance is determined by dividing the sum of squares of the values Y by the number of values. 
   The variance for the alternative sensor noise, σ w   2 , can be determined from the non-speech frames by estimating the sensor noise W t  at each frame of non-speech as:
 
 W   t   =B   t   −GY   t   Eq. 4
 
where G is initially estimated to be zero, but is updated through an iterative process in which σ w   2  is estimated during one step of the iteration and G is estimated during the second step of the iteration. The values of W t  are then used to estimate the variance σ w   2  assuming a zero mean Gaussian model for W.
 
   G estimator  510 , estimates the channel response G during the second step of the iteration as: 
   
     
       
         
           
             
               
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   Where D is the number of frames in which the user is not speaking. In Equation 5, it is assumed that G remains constant through all frames of the utterance and thus is not dependent on the time frame t. 
   Equations 4 and 5 are iterated until the values for σ w   2  and G converge on stable values. The final values for σ v   2 , σ w   2 , and G are stored in model parameters  512 . 
   At step  406 , model parameters for the channel response H are initially estimated by H and σ H   2  estimator  518  using the model parameters for the noise stored in model parameters  512  and the values of B and Y in speech frames  504 . Specifically, H is estimated as: 
                 H   =                 ∑     t   =   1     S     ⁢     (         σ   v   2     ⁢          B   t            -       σ   w   2     ⁢            Y   t          2         )       +                     (       ∑     t   =   1     S     ⁢     (         σ   v   2     ⁢            B   t          2       -       σ   w   2     ⁢            Y   t          2         )       )     2     +     4   ⁢     σ   v   2     ⁢     σ   w   2     ⁢              ∑     t   =   1     S     ⁢       B   t   *     ⁢     Y   t              2                   2   ⁢     σ   v   2     ⁢       ∑     t   =   1     S     ⁢       B   t   *     ⁢     Y   t                     Eq   .           ⁢   6               
where S is the number of speech frames and G is assumed to be zero during the computation of H.
 
   In addition, the variance of a prior model of H, σ H   2 , is determined at step  406 . The value of σ H   2  can be computed as: 
   
     
       
         
           
             
               
                 
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   Under some embodiments, σ H   2  is instead estimated as a percentage of H 2 . For example:
 
σ H   2 =0.01 H   2   Eq. 8
 
   Once the values for H and σ H   2  have been determined at step  406 , these values are used to determine the value of a discriminant function for each speech frame  504  at step  408 . Specifically, for each speech frame, teeth clack detector  514  determines the value of: 
   
     
       
         
           
             
               
                 
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   where K is the number of frequency components in the frequency domain values of B t  and Y t . 
   The present inventors have found that a large value for F t  indicates that the speech frame contains a teeth clack, while lower values for F t  indicate that the speech frame does not contain a teeth clack. Thus, the speech frames can be classified as teeth clack frames using a simple threshold. This is shown as step  410  of  FIG. 4 . 
   Under one embodiment, the threshold for F is determined by modeling F as a chi-squared distribution with an acceptable error rate. In terms of an equation:
 
 P ( F   t &lt;ε|Ψ)=α  Eq. 10
 
where P(F&lt;ε|Ψ) is the probability that F t  is less than the threshold ε given the hypothesis Ψ that this frame is not a teeth clack frame, and α is the acceptable error-free rate.
 
   Under one embodiment, α=0.99. In otherwords, this model will classify a speech frame as a teeth clack frame when the frame actually does not contain a teeth clack only 1% of the time. Using that error rate, the threshold for F becomes ε=365.3650 based on published values for chi-squared distributions. Note that other error-free rates resulting in other thresholds can be used within the scope of the present invention. 
   Using the threshold determined from the chi-squared distribution, each of the frames is classified as either a teeth clack frame or a non-teeth clack frame at step  410 . Because F is dependent on the variance of the background noise and the variance of the sensor noise, the classification is sensitive to errors in determining the values of those variances. To ensure that errors in the variances do not cause too many frames to be classified as containing teeth clacks, teeth clack detector  514  determines the percentage of frames that are initially classified as containing teeth clack. If the percentage is greater than a selected percentage, such as 5% at step  412 , the threshold is increased at step  414  and the frames are reclassified at step  416  such that only the selected percentage of frames are identified as containing teeth clack. Although a percentage of frames is used above, a fixed number of frames may be used instead. 
   Once fewer than the selected percentage of frames have been identified as containing teeth clack, either at step  412  or step  416 , the frames that are classified as non-clack frames  516  are provided to H and σ H   2  estimator  518  to recomputed the values of H and σ H   2 . Specifically, equation 6 is recomputed using the values of B t  and Y t  that are found in non-clack frames  516 . 
   At step  420 , the updated value of H is used with the value of G and the values of the noise variances σ v   2  and σ w   2  by direct filtering enhancement unit  340  to estimate the clean speech value as: 
                   X   t     =       1       σ   w   2     +       σ   v   2     ⁢            H   -   G          2           ⁢     (         σ   w   2     ⁢     Y   t       +       σ   v   2     ⁢       H   *     ⁡     (       B   t     -     GY   t       )           )               Eq   .           ⁢   11               
where H* represent the complex conjugate of H. For frames that are classified as containing teeth clacks, the value of B t  is corrupted by the teeth clack and should not be used to estimate the clean speech signal. For such frames, B t  is estimated as B t ≈HY t  in equation 11. The classification of frames as containing speech and as containing teeth clack is provided to direct filtering enhancement  340  by enhancement model trainer  338  so that this substitution can be made in equation 10.
 
   By estimating H using only those frames that do not include teeth clack, the present invention provides a better estimate of H. This helps to reduce nulls that had been present in the higher frequencies of the clean signal estimates of the prior art. In addition, by not using the alternative sensor signal in those frames that contain teeth clack, the present invention provides a better estimate of the clean speech values for those frames. 
   The flow diagram of  FIG. 4  represents a batch update of the channel responses and the classification of the frames as containing teeth clacks. This batch update is performed across an entire utterance.  FIG. 6  provides a flow diagram of a continuous or “online” method for updating the channel response values and estimating the clean speech signal. 
   In step  600  of  FIG. 6 , an air conduction microphone value, Y t , and an alternative sensor value, B t , are collected for the frame. At step  602 , speech detection unit  500  determines if the frame contains speech. The same techniques that are described above may be used to make this determination. If the frame does not contain speech, the variance for the background noise, the variance for the alternative sensor noise and the estimate of G are updated at step  604 . Specifically, the variances are updated as: 
   
     
       
         
           
             
               
                 
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   where d is the number of non-speech frames that have been processed, and G d-1  is the value of G before the current frame. 
   The value of G is updated as: 
                     G   d     =         J   ⁡     (   d   )       ±           (     J   ⁡     (   d   )       )     2     +     4   ⁢     σ   v   2     ⁢     σ   w   2     ⁢            K   ⁡     (   d   )            2               2   ⁢     σ   v   2     ⁢     K   ⁡     (   d   )             ⁢     
     ⁢     where   ⁢     :               Eq   .           ⁢   14                 J   ⁡     (   d   )       =       cJ   ⁡     (     d   -   1     )       +     (         σ   v   2     ⁢            B   T          2       -       σ   w   2     ⁢            Y   T          2         )               Eq   .           ⁢   15                 K   ⁡     (   d   )       =       cK   ⁡     (     d   -   1     )       +       B   T   *     ⁢     Y   T                 Eq   .           ⁢   16               
where c≦1, provides an effective history length.
 
   If the current frame is a speech frame, the value of F is computed using equation 9 above at step  606 . This value of F is added to a buffer containing values of F for past frames and the classification of those frames as either clack or non-clack frames. 
   Using the value of F for the current frame and a threshold for F for teeth clacks, the current frame is classified as either a teeth clack frame or a non-teeth clack frame at step  608 . This threshold is initially set using the chi-squared distribution model described above. The threshold is updated with each new frame as discussed further below. 
   If the current frame has been classified as a clack frame at step  610 , the number of frames in the buffer that have been classified as clack frames is counted to determine if the percentage of clack frames in the buffer exceeds a selected percentage of the total number of frames in the buffer at step  612 . 
   If the percentage of clack frames exceeds the selected percentage, shown as five percent in  FIG. 6 , the threshold for F is increased at step  614  so that the selected percentage of the frames are classified as clack frames. The frames in the buffer are then reclassified using the new threshold at step  616 . 
   If the current frame is a clack frame at step  618 , or if the percentage of clack frames does not exceed the selected percentage of the total number of frames at step  612 , the current frame should not be used to adjust the parameters of the H channel response model and the value of the alternative sensor should not be used to estimate the clean speech value. Thus, at step  620 , the channel response parameters for H are set equal to their value determined from a previous frame before the current frame and the alternative sensor value B t  is estimated as B t ≈HY t . These values of H and B t  are then used in step  624  to estimate the clean speech value using equation 11 above. 
   If the current frame is not a teeth clack frame at either step  610  or step  618 , the model parameters for channel response H are updated based on the values of B t  and Y t  for the current frame at step  622 . Specifically, the values are updated as: 
                     H   t     =         J   ⁡     (   t   )       ±           (     J   ⁡     (   t   )       )     2     +     4   ⁢     σ   v   2     ⁢     σ   w   2     ⁢            K   ⁡     (   t   )            2               2   ⁢     σ   v   2     ⁢     K   ⁡     (   t   )             ⁢     
     ⁢     where   ⁢     :               Eq   .           ⁢   17                 J   ⁡     (   t   )       =       cJ   ⁡     (     t   -   1     )       +     (         σ   v   2     ⁢            B   T          2       -       σ   w   2     ⁢            Y   T          2         )               Eq   .           ⁢   18                 K   ⁡     (   t   )       =       cK   ⁡     (     t   -   1     )       +       B   T   *     ⁢     Y   T                 Eq   .           ⁢   19               
where J(t−1) and K(t−1) correspond to the values calculated for the previous non-teeth clack frame in the sequence of frames.
 
   The variance of H is then updated as:
 
σ H   2 =0.01| H|   2   Eq. 20
 
   The new values of σ H   2  and H t  are then used to estimate the clean speech value at step  624  using equation 11 above. Since the alternative sensor value B t  is not corrupted by teeth clack, the value determined from the alternative sensor is used directly in equation 11. 
   After the clean speech estimate has been determined at step  624 , the next frame of speech is processed by returning to step  600 . The process of  FIG. 6  continues until there are no further frames of speech to process. 
   Under the method of  FIG. 6 , frames of speech that are corrupted by teeth clack are detected before estimating the channel response or the clean speech value. Using this detection system, the present invention is able to estimate the channel response without using frames that are corrupted by teeth clack. This helps to improve the channel response model thereby improving the clean signal estimate in non-teeth clack frames. In addition, the present invention does not use the alternative sensor values from teeth clack frames when estimating the clean speech value for those frames. This improves the clean speech estimate for teeth clack frames. 
   Although the present invention has been described with reference to particular embodiments, workers skilled in the art will recognize that changes may be made in form and detail without departing from the spirit and scope of the invention.