Multi-sensory speech enhancement using a clean speech prior

A method and apparatus determine a channel response for an alternative sensor using an alternative sensor signal, an air conduction microphone signal. The channel response and a prior probability distribution for clean speech values are then used to estimate a clean speech value.

BACKGROUND

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 is trained using three training channels: a noisy alternative sensor training signal, a noisy air conduction microphone training signal, and a clean air conduction microphone training signal. Each of the signals is converted into a feature domain. The features for the noisy alternative sensor signal and the noisy air conduction microphone signal are combined into a single vector representing a noisy signal. The features for the clean air conduction microphone signal form a single clean vector. These vectors are then used to train a mapping between the noisy vectors and the clean vectors. Once trained, the mappings are applied to a noisy vector formed from a combination of a noisy alternative sensor test signal and a noisy air conduction microphone test signal. This mapping produces a clean signal vector.

This system is less than optimal when the noise conditions of the test signals do not match the noise conditions of the training signals because the mappings are designed for the noise conditions of the training signals.

SUMMARY

A method and apparatus determine a channel response for an alternative sensor using an alternative sensor signal, an air conduction microphone signal. The channel response and a prior probability distribution for clean speech values are then used to estimate a clean speech value.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

FIG. 2is a block diagram of a mobile device200, which is an exemplary computing environment. Mobile device200includes a microprocessor202, memory204, input/output (I/O) components206, and a communication interface208for 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 bus210.

Memory204is 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 memory204is not lost when the general power to mobile device200is shut down. A portion of memory204is preferably allocated as addressable memory for program execution, while another portion of memory204is preferably used for storage, such as to simulate storage on a disk drive.

Memory204includes an operating system212, application programs214as well as an object store216. During operation, operating system212is preferably executed by processor202from memory204. Operating system212, in one preferred embodiment, is a WINDOWS® CE brand operating system commercially available from Microsoft Corporation. Operating system212is preferably designed for mobile devices, and implements database features that can be utilized by applications214through a set of exposed application programming interfaces and methods. The objects in object store216are maintained by applications214and operating system212, at least partially in response to calls to the exposed application programming interfaces and methods.

Communication interface208represents numerous devices and technologies that allow mobile device200to send and receive information. The devices include wired and wireless modems, satellite receivers and broadcast tuners to name a few. Mobile device200can also be directly connected to a computer to exchange data therewith. In such cases, communication interface208can be an infrared transceiver or a serial or parallel communication connection, all of which are capable of transmitting streaming information.

Input/output components206include 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 device200. In addition, other input/output devices may be attached to or found with mobile device200within the scope of the present invention.

FIG. 3provides a basic block diagram of embodiments of the present invention. InFIG. 3, a speaker300generates a speech signal302(X) that is detected by an air conduction microphone304and an alternative sensor306. Examples of alternative sensors include a throat microphone that measures the user'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 microphone304is the type of microphone that is used commonly to convert audio air-waves into electrical signals.

Air conduction microphone304also receives ambient noise308(Z) generated by one or more noise sources310. Depending on the type of ambient noise and the level of the ambient noise, ambient noise308may also be detected by alternative sensor306. However, under embodiments of the present invention, alternative sensor306is typically less sensitive to ambient noise than air conduction microphone304. Thus, the alternative sensor signal316(B) generated by alternative sensor306generally includes less noise than air conduction microphone signal318(Y) generated by air conduction microphone304. Although alternative sensor306is less sensitive to ambient noise, it does generate some sensor noise320(W).

The path from speaker300to alternative sensor signal316can be modeled as a channel having a channel response H. The path from ambient noise308to alternative sensor signal316can be modeled as a channel having a channel response G.

Alternative sensor signal316(B) and air conduction microphone signal318(Y) are provided to a clean signal estimator322, which estimates a clean signal324. Clean signal estimate324is provided to a speech process328. Clean signal estimate324may either be a filtered time-domain signal or a Fourier Transform vector. If clean signal estimate324is a time-domain signal, speech process328may take the form of a listener, a speech coding system, or a speech recognition system. If clean signal estimate324is a Fourier Transform vector, speech process328will typically be a speech recognition system, or contain an Inverse Fourier Transform to convert the Fourier Transform vector into waveforms.

Within direct filtering enhancement322, alternative sensor signal316and microphone signal318are converted into the frequency domain being used to estimate the clean speech. As shown inFIG. 4, alternative sensor signal316and air conduction microphone signal318are provided to analog-to-digital converters404and414, respectively, to generate a sequence of digital values, which are grouped into frames of values by frame constructors406and416, respectively. In one embodiment, A-to-D converters404and414sample the analog signals at 16 kHz and 16 bits per sample, thereby creating 32 kilobytes of speech data per second and frame constructors406and416create a new respective frame every 10 milliseconds that includes 20 milliseconds worth of data.

Each respective frame of data provided by frame constructors406and416is converted into the frequency domain using Fast Fourier Transforms (FFT)408and418, respectively.

The frequency domain values for the alternative sensor signal and the air conduction microphone signal are provided to clean signal estimator420, which uses the frequency domain values to estimate clean speech signal324.

Under some embodiments, clean speech signal324is converted back to the time domain using Inverse Fast Fourier Transforms422. This creates a time-domain version of clean speech signal324.

Embodiments of the present invention provide direct filtering techniques for estimating clean speech signal324. Under direct filtering, a maximum likelihood estimate of the channel response(s) for alternative sensor306are determined by minimizing a function relative to the channel response(s). These estimates are then used to determine a maximum likelihood estimate of the clean speech signal by minimizing a function relative to the clean speech signal.

Under one embodiment of the present invention, the channel response G corresponding to background speech being detected by the alternative sensor is considered to be zero. This results in a model between the clean speech signal and the air conduction microphone signal and alternative sensor signal of:
y(t)=x(t)+z(t)  Eq. 1
b(t)=h(t)*x(t)+w(t)  Eq. 2
where y(t) is the air conduction microphone signal, b(t) is the alternative sensor signal, x(t) is the clean speech signal, z(t) is the ambient noise, w(t) is the alternative sensor noise, and h(t) is the channel response to the clean speech signal associated with the alternative sensor. Thus, in Equation 2, the alternative sensor signal is modeled as a filtered version of the clean speech, where the filter has an impulse response of h(t).

In the frequency domain, Equations 1 and 2 can be expressed as:
Yt(k)=Xt(k)+Zt(k)  Eq. 3
Bt(k)=Ht(k)Xt(k)+Wt(k)  Eq. 4
where the notation Yt(k) represents the kth frequency component of a frame of a signal centered around time t. This notation applies to Xt(k), Zt(k), Ht(k), Wt(k), and Bt(k). In the discussion below, the reference to frequency component k is omitted for clarity. However, those skilled in the art will recognize that the computations performed below are performed on a per frequency component basis.

Under this embodiment, the real and imaginary parts of the noise Ztand Wtare modeled as independent zero-mean Gaussians such that:
Zt=N(O,σz2)  Eq. 5
Wt=N(O,σw2)  Eq. 6
where σz2is the variance for noise Ztand σw2is the variance for noise Wt.

Htis also modeled as a Gaussian such that
Ht=N(H0,σH2)  Eq. 7
where H0is the mean of the channel response and σH2is the variance of the channel response.

Given these model parameters, the probability of a clean speech value Xtand a channel response value Htis described by the conditional probability:
p(Xt,Ht|Yt,Bt,H0,σz2,σw2,σH2)  Eq. 8
which is proportional to:
p(Yt,Bt|Xt,Htσz2σw2)p(Ht|H0σH2)p(Xt)  Eq. 9
which is equal to:
p(Yt|Xt,σz2)p(Bt|Xt,Ht,σw2)p(Ht|H0,σH2)p(Xt)  Eq. 10

In one embodiment, the prior probability for the channel response, p(Ht|H0σH2), is ignored and each of the remaining probabilities is treated as a Gaussian distribution with the prior probability of clean speech, p(Xt), being treated as a zero mean Gaussian with a variance σx,t2such that:
Xt=N(0,σx,t2)  Eq. 11

Using this simplification and Equation 10, the maximum likelihood estimate of Xtfor the frame at t is determined by minimizing:

Since Equation 12 is being minimized with respect to Xt, the partial derivative with respect to Xtmay be taken to determine the value of Xtthat minimizes the function. Specifically,

Xt=σx,t2⁡(σw2⁢Yt+σz2⁢Ht*⁢Bt)σx,t2⁡(σw2+σz2⁢Ht2)⁢+σz2⁢σw2Eq.⁢13
where Ht* represent the complex conjugate of Htand |Ht| represents the magnitude of the complex value Ht.

The channel response Htis estimated from the whole utterance by minimizing:

F=∑t=1T⁢(12⁢σz2⁢Yt-Xt2+12⁢σw2⁢Bt-Ht⁢Xt2)Eq.⁢14
Substituting the expression of Xtcalculated in Equation 13 into Equation 14, setting the partial derivative

∂F∂Ht=0,
and then assuming that H is constant across all time frames T gives a solution for H of:

In Equation 15, the estimation of H requires computing several summations over the last T frames in the form of:

With this formulation, the first frame (t=1) is as important as the last frame (t=T). However, in other embodiments it is preferred that the latest frames contribute more to the estimation of H than the older frames. One technique to achieve this is “exponential aging”, in which the summations of Equation 16 are replaced with:

S⁡(T)=∑t=1T⁢cT-t⁢stEq.⁢17
where c≦1. If c=1, then Equation 17 is equivalent to Equation 16. If c<1, then the last frame is weighted by 1, the before-last frame is weighted by c (i.e., it contributes less than the last frame), and the first frame is weighted by cT−1(i.e., it contributes significantly less than the last frame). Take an example. Let c=0.99 and T=100, then the weight for the first frame is only 0.9999=0.37.

Under one embodiment, Equation 17 is estimated recursively as
S(T)=cS(T−1)+sTEq. 18

Since Equation 18 automatically weights old data less, a fixed window length does not need to be used, and data of the last T frames do not need to be stored in the memory. Instead, only the value for S(T−1) at the previous frame needs to be stored.

The value of c in equations 20 and 21 provides an effective length for the number of past frames that are used to compute the current value of J(T) and K(T). Specifically, the effective length is given by:

The asymptotic effective length is given by:

Thus, using equation 24, c can be set to achieve different effective lengths in equation 19. For example, to achieve an effective length of 200 frames, c is set as:

Once H has been estimated using Equation 15, it may be used in place of all Htof Equation 13 to determine a separate value of Xtat each time frame t. Alternatively, equation 19 may be used to estimate Htat each time frame t. The value of Htat each frame is then used in Equation 13 to determine Xt.

FIG. 5provides a flow diagram of a method of the present invention that uses Equations 13 and 15 to estimate a clean speech value for an utterance.

At step500, frequency components of the frames of the air conduction microphone signal and the alternative sensor signal are captured across the entire utterance.

At step502the variance for ambient noise σz2and the alternative sensor noise σw2is determined from frames of the air conduction microphone signal and alternative sensor signal, respectively, that are captured early in the utterance during periods when the speaker is not speaking.

The method determines when the speaker is not 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. In other embodiments, known speech detection techniques may be applied to the air conduction speech signal to identify when the speaker is speaking. During periods when the speaker is not considered to be speaking, Xtis assumed to be zero and any signal from the air conduction microphone or the alternative sensor is considered to be noise. Samples of these noise values are collected from the frames of non-speech and are used to estimate the variance of the noise in the air conduction signal and the alternative sensor signal.

At step504, the variance of the clean speech prior probability distribution, σx,t2, is determined. Under one embodiment, this variance is computed as:

σx,t2=1(m+k+1)⁢∑d=t-kt+m⁢Yd2-σz2Eq.⁢26
where |Yd|2is the energy of the air conduction microphone signal and the summation is performed over a set of speech frames that includes the k speech frames before the current speech frame and the m speech frames after the current speech frame. To avoid a negative value or a value of zero for the variance, σx,t2, some embodiments of the present invention use (0.01·σz2) as the lowest possible value for σx,t2.

In an alternative embodiment, a real-time implementation is realized using a smoothing technique that relies only on the variance of the clean speech signal in the preceding frame of speech such that:
σx,t2=pmax(|Yd|2−σz2,α|Yd|2)+(1−p)σx,t−12Eq. 27
where σx,t−12is the variance of the clean speech prior probability distribution from the last frame that contained speech, p is a smoothing factor with a range between 0 and 1, α is a small constant, and max(|Yd|2−σz2,α|Yd|2) indicates that the larger of |Yd|2−σz2and α|Yd|2is selected to insure positive values for σx,t2. Under one specific embodiment, the smoothing factor has a value of 0.08, and α=0.01.

At step506, the values for the alternative sensor signal and the air conduction microphone signal across all of the frames of the utterance are used to determine a value of H using Equation 15 above. At step508, this value of H is used together with the individual values of the air conduction microphone signal and the alternative sensor signal at each time frame to determine an enhanced or noise-reduced speech value for each time frame using Equation 13 above.

In other embodiments, instead of using all of the frames of the utterance to determine a single value of H using Equation 15, Htis determined for each frame using Equation 19. The value of Htis then used to compute Xtfor the frame using Equation 13 above.

In a second embodiment of the present invention, the channel response of the alternative sensor to ambient noise is considered to be non-zero. In this embodiment, the air conduction microphone signal and the alternative sensor signal are modeled as:
Yt(k)=Xt(k)+Zt(k)  Eq. 28
Bt(k)=Ht(k)Xt(k)+Gt(k)Zt(k)+Wt(K)  Eq. 29
where the alternative sensors channel response to the ambient noise is a non-zero value of Gt(k).

The maximum likelihood for the clean speech Xtcan be found by minimizing an objective function resulting in an equation for the clean speech of:

In order to solve Equation 30, the variances σx,t2, σw2and σz2as well as the channel response values H and G must be known.FIG. 6provides a flow diagram for identifying these values and for determining enhanced speech values for each frame.

In step600, frames of the utterance are identified where the user is not speaking. These frames are then used to determine the variance σw2and σz2for the alternative sensor and the ambient noise, respectively.

To identify frames where the user is not speaking, the alternative sensor signal can be examined. Since the alternative sensor signal will produce much smaller signal values for background speech than for noise, if the energy of the alternative sensor signal is low, it can be assumed that the speaker is not speaking.

After the variances for the ambient noise and the alternative sensor noise have been determined, the method ofFIG. 6continues at step602where it determines the variance of the clean speech prior probability, σx,t2, using equations 26 or 27 above. As discussed above, only those frames containing speech are used to determine the variance of the clean speech prior.

At step604, the frames identified where the user is not speaking are used to estimate the alternative sensor's channel response G for ambient noise. Specifically, G is determined as:

Where D is the number of frames in which the user is not speaking. In Equation 31, it is assumed that G remains constant through all frames of the utterance and thus is no longer dependent on the time frame t. In equation 31, the summation over t may be replaced with the exponential decay calculation discussed above in connection with equations 16-25.

At step606, the value of the alternative sensor's channel response G to the background speech is used to determine the alternative sensor's channel response to the clean speech signal. Specifically, H is computed as:

In Equation 32, the summation over T may be replaced with the recursive exponential decay calculation discussed above in connection with equations 16-25.

After H has been determined at step606, Equation 30 may be used to determine a clean speech value for all of the frames. In using Equation 30, under some embodiments, the term Bt−GYtis replaced with

(1-GYtBt)⁢Bt
because it has been found to be difficult to accurately determine the phase difference between the background speech and its leakage into the alternative sensor.

If the recursive exponential decay calculation is used in place of the summations in Equation 32, a separate value of Htmay be determined for each time frame and may be used as H in equation 30.