Voice Activity Detection (VAD) • Problem: Determine if voice is present in a particular audio signal. • Issues: loud noise classified as speech and soft speech classified as noise • Applications – Speech Recognition – Speech transmission – Speech enhancement • Increases performance of speech applications more than any other single component • Goal: extract features from a signal that emphasize differences between speech and background noise
Voice Activity Detection (VAD). Problem : Determine if voice is present in a particular audio signal. Issues: loud noise classified as speech and soft speech classified as noise Applications Speech Recognition Speech transmission Speech enhancement - PowerPoint PPT Presentation
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Voice Activity Detection (VAD)• Problem: Determine if voice is present in a particular
audio signal. • Issues: loud noise classified as speech and soft speech
• Increases performance of speech applications more than any other single component
• Goal: extract features from a signal that emphasize differences between speech and background noise
General Signal Characteristics• Energy compared to long term noise estimates
– K. Srinivasan, A. Gersho, “Voice activity detection for cellular networks,” Proc. Of the IEEE Speech Coding Workshop, Oct 1993, pp. 85-86
• Likelihood ratio based on statistical methods– Y.D. Cho, K. Al-Naimi, A. Kondoz, “Improved voide activity
detection based on a smoothed statistical likelihood ratio,” Proceedings ICASSP, 2001, IEE Press
• Compute the kurtosis– R. Gaubran, E. Nemer and S.Mahmoud, “SNR estimation of
speech signals using subbands and fourth-order statistics,” IEEE Signal Processing Letters, vol. 6, no. 7, pp. 171-174, 1999
Extract Features in Speech Model• Presence of pitch
– “Digital cellular telecommunication system (phase 2+); voice activity detector for adaptive multi-rate (amr) speech traffic channels,” ETSI Report, DEN/SMG-110694Q7, 2000
• Formant shape– J.D. Hoyt, H.Wechsler, “Detection of human speech in
structured noise,” Proc. IEEE International Conference on Acoustics, Speech , and Signal Processing, 1994, pp. 237-240
• Cepstrum– J.A. Haigh, J.S. Mason, “Robust voice activity detection using
cepstral features,” IEEE TEN-CON, pp. 321-324, 1993
Multi-channel Algorithms
• Utilize additional information provided by additional sensors– P. Naylor, N. Doukas, T. Stathaki, “Voice activity
detection using source separation techniques,” Proc. Eurospeech, 1997, pp. 1099-1102
– J.F. Chen, W. Ser, “Speech detection using microphone array,” Electronic Letters, vol 36(2), pp. 181-182, 2000
– Q. Zou, X. Zou, M. Zhang, Z. Lin, “A robust speech detection algorithm in a microphone array teleconferencing system,” Proc. ICASSP, 2001, IEEE Press
Statistics: Mean
• First moment - Mean or average value: μ = ∑i=1,N si
• Second moment - Variance or spread: σ2 = 1/N∑i=1,N(si - μ)2
• Standard deviation – probability of distance from mean: σ • 3rd standardized moment- Skewness: γ1 = 1/N∑i=1,N(si-μ)3/σ3
– Negative tail: skew to the left– Positive tail: skew to the right
• 4th standardized moment – Kurtosis: γ2 = 1/N∑i=1,N(si-μ)4/σ4
– Level estimated during periods of low energy– Adaptive estimate: The noise floor estimate lowers quickly
and raises slowly when encountering non-speech frames• Energy: Speech energy significantly exceeds the noise level
• Cepstrum Analysis – Voiced speech contains F0 plus harmonics that will show as
a Cepstrum peak related to that periodicity and to voice.– Flat Cepstrums can result from a door slam or clap
• Kurtosis: Linear prediction coding clean voiced speech residuals have a large kurtosis
Likelihood Ratio Test (LRT)• L.Sohn, N.S. Kim, W.Sung, “A statistical model-based voice
activity detection,” IEEE Signal Processing Letters, vol. 6, no.1, pp. 1-3, Jan 1999
• J. Ramirez, J.C. Segura, et. al., “Statistical voice activity detection using a multiple observation likelihood ratio test,” IEEE Signal Processing letters, vol. 12, no. 10, pp. 689-692, Oct 2005
• Utilizes the geometric mean:
GM = (∏1,nai)1/n= e1/n∑1,n
ln(ai))
log(GM) = log (∏1,nai)1/n) = 1/n log(∑1,nai)
Geometric Mean• Arithmetic mean: applicable when using numeric quantities
– Annual growth: 2.5, 3, and 3.5 million dollars• Geometric mean: applicable when using percentages
– Company grows annually by 2.5, 3, and 3.5%• Example: A company starts with $1,000,000
– Assets grow by 2.5, 3, and 3.5 percent over three years– Arithmetic mean: 1/N∑i=1,Ngi = (1.025 + 1.03 + 1.035)/3 = 1.03
• Apply the auto-correlation formula to estimate pitchRf[z] = ∑i=1,n-z xf[i]xf[i+z]/∑i=1,F xf[i]2
M[k] = max(rf[z])
• Expectation: Voiced speech should produce a higher M[k] than unvoiced speech, silence, or noise frames
• Notes: – We can do the same thing with Cepstrals– Auto-correlation complexity improved by limiting the Rf[z]
values that we bother to compute
Zero Crossing
• The effectiveness of auto correlation decreases as SNR approaches zero
• Enhancement to Auto Correlation method when SNR values are low
• Algorithm – Eliminate the pre-emphasis step (preserve the
original pitch)– Assume every two zero crossings is a pitch period– Auto correlate each period with its predecessor
Use of Entropy as VAD MetricFOR each frame
Decompose the signal into 24 Bark (or Mel) scale bandsCompute the energy in each frequency bandFOR each band of frequenciesenergy[band] = ∑i=bstart,bend
|x[i]|2
IF an initial or low energy frame, noise[band] = energy[band]ELSE speech[band] = energy[band] – noise[band]Sort speech[band] and select subset of bands with max speech[band] valuesCompute the probability of energy[band]/totalEnergyCompute entropy = - ∑useful bandsP(energy[band]) log(P(energy[band]))
Note: We expect higher entropy in noise; signal, should be organizedAdaptive Noise adjustment: for frame f and 0 < α <1
1. Analyze a larger window and classify based on the percentage of time voice appears to be present or absent
2. Focus on the change of signal peak energy at the onset and termination of speech.
a. Onset: drastic increase in peak energyb. Continuous speech: intermittent peak spikesc. Termination: absence of peak spikes and energy
Evaluating ASR Performance• Importance of VAD
– The VAD component impacts speech recognition the most– Without VAD, ASR accuracy degrades to less than 30% in
noisy environments
• Evaluation standards– Without an objective standard, researchers will not be
able evaluate how various algorithms impact ASR accuracy– H.G. Hirsch, D. Pearce, “The Aurora experimental
framework for the performance evaluation of speech recognition systems under noisy conditions,” Proc. ISCA ITRW ASR2000, vol. ASSP-32, pp. 181-188, Sep. 2000