Method of determining variable-length frame for speech signal preprocessing and speech signal preprocessing method and device using the same

• US 20050240397A1
• Filed: 04/22/2005
• Published: 10/27/2005
• Est. Priority Date: 04/22/2004
• Status: Abandoned Application

• Current Assignee: Samsung Electronics Co. Ltd.
• Original Assignee: Samsung Electronics Co. Ltd.
• Inventors: Jeon, Bum-Ki
• Application Number:
  US11/111,941
• Publication Number:
  US 20050240397A1
• Time in Patent Office: Days
• US Class Current: 704/219
• CPC Class Codes: G10L 15/02 Feature extraction for speech recognition; Selection of recognition unit
• CPC Class Codes: G10L 19/265 Pre-filtering, e.g. high frequency emphasis prior to encoding
• CPC Class Codes: G10L 25/12 the extracted parameters being prediction coefficients

Abstract

Disclosed are a device and a method of determining a variable-length frame for speech signal preprocessing, which can improve performance of speech signal processing during a speech signal preprocessing procedure, and a speech signal preprocessing method and device using such a preprocessing method. The preprocessing method includes the steps of converting the input speech signal into a digital speech signal, varying a frame length of the speech signal and simultaneously calculating an LPC residual error from frame length to frame length, and determining a length of the current frame by taking a frame length at which the LPC residual error is minimal. The speech signal preprocessing method and device use the processing method uses a variable-length frame. These methods and device can extract a more accurate feature vector, thereby preventing lower recognition in performance during speech signal processing.

Claims

1. A frame processing method for dividing a speech signal into a plurality of frames in order to extract a feature vector of an input speech signal, the method comprising the steps of:

(1) converting the input speech signal into a digital speech signal;

(2) varying a frame length of the speech signal and simultaneously calculating a Linear Prediction Coefficient (LPC) residual error from frame length to frame length; and

(3) determining a length of the current frame by taking a frame length at which the LPC residual error is minimal.

2. The method as claimed in claim 1, wherein step (2) is repeatedly performed from a predetermined minimum frame length to a predetermined maximum frame length.

3. The method as claimed in claim 1, wherein the frame length is determined in a range of 20 ms to 45 ms.

4. The method as claimed in claim 1, further comprising the step of:

(4) multiplying the frame length determined at step (3) by a weighting value w_i as defined below by Equation (4);

$\begin{array}{cc}{w}_i=\frac{t<br><br>\text{-}<br><br>\mathrm{th}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{frame}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{length}}{\mathrm{maximum}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{frame}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{length}}.& \mathrm{Equation}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(4\right)\end{array}$

5. The method as claimed in claim 1, wherein a starting point of the current frame of which the LPC residual error is calculated at step (2) is set to a midpoint of the previous frame.

6. A speech signal preprocessing method for extracting a feature vector of a speech signal, the method comprising the steps of:

(1) converting an input speech signal into a digital signal;

(2) performing pre-emphasis filtering for emphasizing a high-frequency band of the speech signal;

(3) varying a frame length of the speech signal and simultaneously calculating a Linear Prediction Coefficient (LPC) residual error from frame length to frame length;

(4) determining a length of each frame by taking a frame length at which the LPC residual error is minimal; and

(5) extracting a feature vector of the speech signal from each frame.

7. The method as claimed in claim 6, wherein step (3) is repeatedly performed from a predetermined minimum frame length to a predetermined maximum frame length.

8. The method as claimed in claim 6, further comprising:

(6) multiplying the frame length determined at step (3) by a weighting value w_i as defined below by Equation (4);

$\begin{array}{cc}{w}_i=\frac{t<br><br>\text{-}<br><br>\mathrm{th}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{frame}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{length}}{\mathrm{maximum}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{frame}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{length}}.& \mathrm{Equation}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(4\right)\end{array}$

9. The method as claimed in claim 6, wherein at step (5), the feature vector is expressed by a delta Cepstrum as defined below by Equation (9):

$\begin{array}{cc}\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>c<br><br>\left(n\right)=\frac{\left[\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>c<br><br>\left(n+t\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>l_n<br><br>\left(t\right)-\frac{1}{2<br><br>M+1}<br><br>\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>l_n<br><br>\left(t\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>c<br><br>\left(n+t\right)\right]}{\left[\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>l_n^2<br><br>\left(t\right)-\frac{1}{2<br><br>M+1}<br><br>{\left(\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>l_n<br><br>\left(t\right)\right)}^2\right]}& \mathrm{Equation}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(9\right)\end{array}$

where, Δ

c(n), c(n) and l_n(t) denote a delta Cepstrum of the n-th frame, a Cepstrum of the n-th frame and a distance between the n-th frame and (n+1)-th frame, respectively.

10. A speech signal preprocessing device comprising:

an analog-to-digital converter for converting an input speech signal into a digital signal;

a pre-emphasis filter for performing pre-emphasis filtering which emphasizes a high-frequency band of the speech signal;

a framing processor for varying a frame length of the speech signal and simultaneously calculating a Linear Prediction Coefficient (LPC) residual error from frame length to frame length, and determining a length of each frame by taking a frame length at which the LPC residual error is minimal; and

a feature vector extractor for extracting a feature vector from each frame.

11. The device as claimed in claim 10, wherein the framing processor is constructed such that it calculates the LPC residual error from a predetermined minimum frame length to a predetermined maximum frame length.

12. The device as claimed in claim 10, wherein the framing processor is further constructed such that it multiplies the determined frame length by a weighting value w_i as defined below by Equation (4):

$\begin{array}{cc}{w}_i=\frac{t-\mathrm{th}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{frame}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{length}}{\mathrm{maximum}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{frame}<br><br>\text{\hspace{1em}<br><br>}<br><br>\mathrm{length}}.& \mathrm{Equation}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(4\right)\end{array}$

13. The device as claimed in claim 10, wherein the feature vector extractor is constructed such that it derives the feature vector using a delta Cepstrum as defined below by Equation (9):

$\begin{array}{cc}\mathrm{\Delta <br><br>}<br><br>\text{\hspace{1em}<br><br>}<br><br>c<br><br>\left(n\right)=\frac{\left[\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>c<br><br>\left(n+t\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>l_n<br><br>\left(t\right)-\frac{1}{2<br><br>M+1}<br><br>\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>l_n<br><br>\left(t\right)<br><br>\text{\hspace{1em}<br><br>}<br><br>\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>c<br><br>\left(n+t\right)\right]}{\left[\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>l_n^2<br><br>\left(t\right)-\frac{1}{2<br><br>M+1}<br><br>{\left(\underset{t=-M}{\overset{t=M}{\sum <br><br>}}<br><br>l_n<br><br>\left(t\right)\right)}^2\right]}& \mathrm{Equation}<br><br>\text{\hspace{1em}<br><br>}<br><br>\left(9\right)\end{array}$

where, Δ

c(n), c(n) and l_n(t) denote a delta Cepstrum of the n-th frame, a Cepstrum of the n-th frame and a distance between the n-th frame and (n+1)-th frame, respectively.