How to Accurately Extract Fundamental Frequency from Speech Signals?

[jishact]

what are the preprocessing steps to be performed on a speech signal in order to extract its fundamental frequency accurately? which sampling frequencies and cut off frequencies are to selected to get a better result?

 

[berkeman]

what are the preprocessing steps to be performed on a speech signal in order to extract its fundamental frequency accurately? which sampling frequencies and cut off frequencies are to selected to get a better result?

Welcome to the PF. Why don't you tell us what you know about this subject. What would be the advantages and disadvantages of using analog versus digital pre-processing? What would be a good mix of analog and digital technologies in the pre-processing subsystem?

And what the heck is the "fundamental frequency" of speech? Have you looked into the fundamentals of speech recognition to see what kind of processing is involved?

http://en.wikipedia.org/wiki/Speech_recognition

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[Mene]

I would like to start by clarifying that the preprocessing steps for speech signals may vary depending on the specific goals and applications of the research. However, some common steps for extracting the fundamental frequency accurately from a speech signal include:

1. Pre-emphasis: This step helps to amplify the high-frequency components of the speech signal and improve its overall quality. It involves applying a high-pass filter to the signal.

2. Framing: Speech signals are continuous, so it is essential to divide them into smaller frames for analysis. This step helps to capture the variations in the fundamental frequency over time.

3. Windowing: After framing, the signal is multiplied by a window function to reduce the spectral leakage and improve the accuracy of the frequency estimation.

4. Pitch detection: This step involves using algorithms to estimate the fundamental frequency of each frame. Some common methods include autocorrelation, cepstrum analysis, and harmonic product spectrum.

Regarding the selection of sampling frequencies and cut-off frequencies, it is crucial to consider the Nyquist frequency, which states that the sampling frequency must be at least twice the highest frequency present in the signal. For speech signals, the highest frequency is typically around 4 kHz, so a sampling frequency of at least 8 kHz is recommended.

As for the cut-off frequencies, they depend on the specific characteristics of the speech signal. It is essential to consider the bandwidth of the signal and the desired frequency range for analysis. Generally, a low-pass filter with a cut-off frequency of around 4 kHz is suitable for speech signals.

In conclusion, the preprocessing steps for accurately extracting the fundamental frequency from a speech signal involve pre-emphasis, framing, windowing, and pitch detection. The selection of sampling and cut-off frequencies should follow the Nyquist theorem and consider the characteristics of the signal. Continual improvements in signal processing techniques can further enhance the accuracy of fundamental frequency estimation in speech signals.