Claims
- 1. A method for segmenting an audio signal including a plurality of frames, comprising:
extracting high-dimensional features from the audio signal; projecting non-linearly the high-dimensional features to low-dimensional features; averaging the low-dimensional features; applying a linear discriminant to determine a threshold separating the low-dimensional features; classifying each frame of the audio signal as either non-speech or speech using the threshold.
- 2. The method of claim 1 wherein the audio signal is continuous.
- 3. The method of claim 2 further comprising:
updating the threshold continuously.
- 4. The method of claim 1 wherein the high-dimensional features have twenty-six dimensions and the low-dimensional features have two dimensions.
- 5. The method of claim wherein each dimension is a monotonic function.
- 6. The method of claim 5 wherein the monotonic function is a logarithm of a probability of each feature.
- 7. The method of claim 1 wherein the non-linear projection is a likelihood projection.
- 8. The method of claim 1 further comprising:
projecting the low-dimensional features onto an axis as a one-dimensional projection.
- 9. The method of claim 8 wherein a histogram of the one-dimensional projection has a bi-modal distribution connected by an inflection point defining the threshold.
- 10. The method of claim 1 further comprising:
representing each frame of the audio signal as a weighted average of likelihood-difference values of a window of frames around each frame.
- 11. The method of claim 9 further comprising:
fitting a Gaussian mixture distribution to the bi-modal distribution to determine the threshold.
- 13. The method of claim 11 wherein the Gaussian mixture distribution is determined using an expectation maximization process.
- 14. The method of claim 9 further comprising:
fitting a polynomial function to the bi-modal distribution to determine the threshold.
- 15. The method of claim 14 wherein the polynomial function is a logarithm of a distribution of the histogram.
- 16. The method of claim 1 wherein the audio signal is processed in batch-mode.
- 17. The method of claim 16 wherein an averaging window is symmetric.
- 18. The method of claim 17 wherein the averaging window is rectangular.
- 19. The method of claim 17 wherein the averaging window is a Hamming window.
- 20. The method of claim 1 wherein the audio signal is processed in real-time.
- 21. The method of claim 20 wherein an averaging window is asymmetric.
- 22. The method of claim 20 wherein the averaging window is constructed using two unequal sized Hamming windows.
- 23. The method of claim 1 wherein the high-dimensional features include spectral patterns and temporal dynamics of the audio signal.
- 24. The method of claim 1 wherein the high-dimensional features is a short-term Fourier transform of the audio signal.
- 25. The method of claim 1 further comprising:
merging adjacent identically classified frames into segments.
- 26. The method of claim 25 further comprising:
discarding speech segments shorter than a predetermined length.
- 27. The method of claim 26 wherein the predetermined length of time is ten milliseconds.
- 28. The method of claim 27 further comprising:
extending each speech segment at a beginning and an end by about half a width of an averaging window.
STATEMENT REGARDING FEDERALLY-SPONSORED RESEARCH
[0001] This invention was made with United State Government support awarded by the Space and Naval Warfare Systems Center, San Diego, under Grant No. N66001-99-1-8905. The United State Government has rights in this invention.