Joint estimation of formant trajectories via bayesian techniques and adaptive segmentation

• US 7,881,926 B2
• Filed: 09/20/2007
• Issued: 02/01/2011
• Est. Priority Date: 09/29/2006
• Status: Active Grant

• Current Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Original Assignee: Honda Research Institute Europe GmbH (Honda Motor Company)
• Inventors: Glaeser, Claudius, Heckmann, Martin, Joublin, Frank
• Primary Examiner(s): Hudspeth; David R
• Assistant Examiner(s): Neway; Samuel G
• Application Number:
  US11/858,743
• Publication Number:
  US 20080082322A1
• Time in Patent Office: 1,230 Days
• Field of Search: None
• US Class Current: 704/209
• CPC Class Codes: G10L 25/15 the extracted parameters being formant information
• CPC Class Codes: G10L 25/48 specially adapted for particular use

Abstract

The invention relates to the field of automated processing of speech signals and particularly to a method for tracking the formant frequencies in a speech signal, comprising the steps of: obtaining an auditory image of the speech signal; sequentially estimating formant locations; segmenting the frequency range into sub-regions; smoothing the obtained component filtering distributions; and calculating exact formant locations.

Claims

1. A computer based method of tracking formant frequencies in a speech signal, the method comprising:

obtaining a spectrogram on the speech signal;

obtaining component filtering distributions by applying Bayesian Mixture Filtering to the spectrogram;

segmenting a frequency range into sub-regions based on the component filtering distributions;

smoothing the obtained component filtering distributions using Bayesian smoothing; and

calculating exact formant locations based on the smoothed component filtering distributions.

2. The method of claim 1, wherein a joint distribution Bel(x_t) of a recursive Bayesian filter is expressed as

3. The method of claim 2, wherein prediction of the recursive Bayesian filter is expressed as

4. The method of claim 1, wherein the segmenting step includes the step of calculating an optimal path according to a cost function.

5. The method of claim 4, wherein the optimal path for the segmenting is calculated using Viterbi algorithm.

6. The method of claim 4, wherein the optimal path for the segmenting is calculated using Dijkstra algorithm.

7. The method of claim 1, further comprising learning a motion model of Bayesian filtering.

8. The method of claim 7, wherein the learning of the motion model of the Bayesian filtering of a current time step takes previous time steps into account.

9. The method of claim 7, wherein the learning of the motion model of the Bayesian filtering takes interaction of the different formants into account.

10. The method of claim 1, wherein smoothing the obtained component filtering distributions comprises Bayesian smoothing.

11. The method of claim 10, wherein the Bayesian smoothing recursively estimates smoothing distribution of states based on predefined system dynamics p(x_t+1|x_t) and filtering distribution Bel(x_t) of the states, where p(x_t+1/x_t) is a probability distribution over possible formant locations x at time t+1, given knowledge about formant locations at time t.

12. The method of claim 1, further comprising preprocessing of the speech signal, and performing speech recognition based on the exact formant locations.

13. The method of claim 1, further comprising performing artificial formant-based speech synthesis based on the exact formant locations.

14. A computer program product comprising a non-transitory computer readable medium structured to store instructions executable by a processor in a computing device, the instructions, when executed cause the processor to:

obtain a spectrogram on a speech signal;

obtain component filtering distribution by applying Bayesian Mixture Filtering of the spectrogram;

segment a frequency range into sub-regions based on the component filtering distributions;

smooth the obtained component filtering distributions using Bayesian smoothing; and

calculate exact formant locations based on the smoothed component filtering distributions.