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I'm reading “Speech segmentation without speech recognition” by Dong Wang, Lie Lu and Hong-Jiang Zhang. The algorithm I'm looking at is a V/C/P (Vowel/Consonant/Pause) classification algorithm on a digital speech signal. It is described as such:

  1. Audio data is segmented into 20ms-long non-overlapping frames, where features, including ZCR, Energy and Pitch, are extracted.

  2. Energy and pitch curve is smoothed.

  3. The Mean_En and Std_En of energy curve are calculated to coarsely estimate the background noise energy level, as:

    NoiseLevel = Mean_En - 0.75 Std_En. 

    Similarly the threshold of ZCR (ZCR_dyna) is defined as:

    ZCR_dyna = Mean_ZCR + 0.5 Std_ZCR
  4. Frames are classified as V/C/P coarsely by using the following rules, where FrameType is used to denote the type of each frame.

    If ZCR > ZCR_dyna then FrameType = Consonant 
    Else if Energy < NoiseLevel, then  FrameType = Pause 
    Else FrameType = Vowel   
  5. Update the NoiseLevel as the weighted average energy of the frames at each vowel boundary and the background segments.

  6. Re-classify the frames using algorithm in step 4 with the updated NoiseLevel. Pauses are merged by removing isolated short consonants. Vowel will be split at its energy valley if its duration is too long

I do not understand step #5. Like I don't know how to interpret the wording - is there another way to describe what they are doing? I get that we want to update the NoiseLevel variable and re-run step #4 for every frame, I just don't understand how exactly.

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Maybe you can ask the authors for clarification? – mayank Feb 5 '13 at 17:48
Yeah I can try that, though I was hoping it would be obvious to someone else so I could get a quicker response =) – YoungMoney Feb 5 '13 at 17:54

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