Robust Entropy-based Endpoint Detection for Speech Recognition in Noisy Environments

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Robust Entropy-based Endpoint Detection for
Speech Recognition in Noisy Environments

• ???
• Jang_at_cs.nthu.edu.tw
• http//www.cs.nthu.edu.tw/jang

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Reference

• Jialin Shen, Jeihweih Hung, Linshan Lee, Robust
  entropy-based endpoint detection for speech
  recognition in noisy environments, International
  Conference on Spoken Language Processing, Sydney,
  1998

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Summary

• Entropy-based algorithm for accurate and robust
  endpoint detection for speech recognition under
  noisy environments
• Better than energy-based algorithms in both
  detection accuracy and recognition performance
• Error reduction 16

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Motivation

• Energy-based endpoint detection becomes less
  reliable when dealing with non-stationary noise
  and sound artifacts such as lip smacks, heavy
  breathing and mouth clicks, etc.
• Spectral entropy is effective in distinguishing
  the speech segments from the non-speech parts.

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Spectral Entropy

• PDF
• Normalization
• Spectral entropy

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Properties of Entropy

• N2
• entropyPlot.m

• N3

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Entropy Weighting

• A set of weighting factors can be applied
• These weighting factors are statistically
  estimated from a large collection of speech
  signals.

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Endpoint Detection

• The sum of the spectral entropy values over a
  duration of frames (20 frames) is first evaluated
  and smoothed by a median filter
• Some thresholds are used to detect the beginning
  and ending boundaries of the embedded speech
  segments
• A short period of background noise is first taken
  as the reference for some initial boundary
  detection process.
• Short speech segments (lt100ms) are rejected.

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Experiment Settings

• Speech database
• Isolated digits in Mandarin Chinese produced by
  100 speakers (10 speakers for test, others for
  training)
• Speech features 12-order MFCC and 12-order delta
  MFCC
• Models
• Continuous-density HMM
• 6 states/digits, 3 mixture/state

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Experiment Settings

• Noise
• NOISEX-92 noise-in-speech database
• White noise, pink noise, volvo noise (car noise),
  F16 noise, machinegun noise
• Sound artifacts
• Breath noise, cough noise and mouse click noise.

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Example
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Experimental Results
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Experimental Results
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Something Not Clear

• What is the sample rate? Bit resolution?
• What is the frame size and overlap?
• What is the order of the median filter?
• How to use the short period of background
  noise?
• What is the value for the thresholds of spectral
  entropy for determining boundaries?
• What are the values for d1 and d2?