1. By Sarita Jondhale1 Pattern Comparison Techniques

2. By Sarita Jondhale2 Pattern Comparison Techniques The output of the front end spectral analysis is in the form of vectors. The test pattern T is the set containing many vectors. The reference pattern R is the set containing many vectors. The goal of pattern comparison stage is to determine the dissimilarity of each vector in T to each vector of R The reference pattern should be such that there should be minimum dissimilarity

3. By Sarita Jondhale3 Pattern Comparison Techniques To determine the global similarity of T and R we will consider the following problems: T and R generally are of unequal length w.r.t. time duration due to different speaking rates across different talkers T and R need not line up in time in any simple or well prescribed manner this is because different sounds cannot be varied in duration to same degree. Vowels are easily lengthened or shortened but consonants cannot change in duration We need a way to compare a spectral vectors

4. By Sarita Jondhale4 Speech Detection Also called as End point detection The goal of speech detection is to separate speech signal with a background signal. The need of speech detection occurs in many applications in telecommunications For automatic speech recognition, end point detection is required to isolate the speech of interest so as to be able to create a speech pattern or template.

5. By Sarita Jondhale5 Speech Detection Speech must be detected so as to provide the best patterns for the recognition Best patterns means which provides highest recognition accuracy

6. By Sarita Jondhale6 Speech Detection Accurate detection of speech is a simple problem when speech is produced in a relatively noise free environment It becomes difficult task when the environment is noisy

7. By Sarita Jondhale7 Speech Detection First factor: during speech, talker produces sound like lip smacks, heavy breathing and mouth clicks Mouth click with speaking: The mouth click is produced by opening the lips prior to speaking or after speaking, the noise of clicking is separate from the speech signal and the energy level is comparable to speech energy signal

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10. 10 Speech Detection Heavy breathing with speaking: unlike the mouth click the heavy breathing noise is not separated from the speech and therefore makes accurate end point detection quite difficult

11. By Sarita Jondhale11 Speech Detection Second factor: environmental noise The ideal environment for talking is the quite room with no acoustic noise signal generators other than that produced by the speaker.

12. By Sarita Jondhale12 Speech Detection Ideal environment is not possible practically Have to consider speech produced In noisy backgrounds (fans, machinery) In non stationary environments (presence of door slams, irregular road noise, car horns) With speech interference ( as from TV, radio, or background conversations) And in hostile circumstances ( when the speaker is stressed)

13. By Sarita Jondhale13 Speech Detection These interfering signals are some what like speech signals therefore accurate end point detection become difficult

14. By Sarita Jondhale14 Speech Detection Third factor: distortion introduced by the transmission system over which speech signal is sent.

15. By Sarita Jondhale15 Speech Detection The methods for speech detection is broadly classified into three approaches The explicit approach The implicit approach The hybrid approach

16. By Sarita Jondhale16 The explicit approach

17. By Sarita Jondhale17 The explicit approach The speech signal is first measured and feature measurement is made The speech detection method is then applied to locate and define the speech events The detected speech is sent to the pattern comparison algorithm, and finally the decision mechanism chooses the recognized word

18. By Sarita Jondhale18 The explicit approach For signals with a stationary and low level noise background, the approach produces reasonably good detection accuracy The approach fails often when the environment is noisy or the interference in non stationary

19. By Sarita Jondhale19 The implicit approach

20. By Sarita Jondhale20 The implicit approach This approach detects the speech detection problem simultaneously with the pattern matching and recognition-decision process It recognizes that the speech events are almost always accompanied by a certain acoustic background

21. By Sarita Jondhale21 The implicit approach The unmarked signal sequence is processed by the pattern matching module in which all possible end points sets are considered The decision mechanism provides ordered list of the candidate words as well as corresponding speech locations The final result is best candidate and its associated end points.

22. By Sarita Jondhale22 The implicit approach Depending on the word recognized the boundary locations could inherently be different with the implicit method (feedback) With explicit method only a single choice of boundary locations is made

23. By Sarita Jondhale23 The implicit approach

24. By Sarita Jondhale24 The implicit approach Advantages & disadvantages Requires heavy computations But offers higher detection accuracy than the explicit approach

25. By Sarita Jondhale25 The hybrid approach This is the combination of both implicit and explicit approaches Uses the explicit method to obtain several end points sets for recognition processing and implicit method to choose the alternatives The most likely candidate word and the corresponding end points as in implicit approach, are provided by the decision box.

26. By Sarita Jondhale26 The hybrid approach

27. By Sarita Jondhale27 The hybrid approach Computational load is equivalent to explicit method And accuracy comparable to implicit method

28. By Sarita Jondhale28 Speech activity detection algorithm

29. By Sarita Jondhale29 Speech activity detection algorithm Adaptive level equalization module: estimates the level of the acoustic background and uses the result to equalize the measured energy contour Preliminary energy pulses, which are speech like bursts are detected from the equalized energy contour Finally, these energy pulse end points are ordered to determine the possible sets of word end point pairs Contour: A line drawn on a map connecting points of equal height