What is the principle of Linear Predictive Coding (LPC) in speech synthesis?

Linear Predictive Coding (LPC) is a method used in speech synthesis to model the human vocal tract and predict the current sample of a speech signal based on its previous samples. The core principle is that speech can be approximated as a linear combination of past samples, filtered through a time-varying all-pole filter that represents the vocal tract's resonant characteristics.

The LPC algorithm analyzes a speech signal to extract a set of linear prediction coefficients (LPCs), which define the filter. These coefficients are derived by minimizing the mean squared error between the actual speech signal and the predicted signal. The process involves:

1. Autocorrelation or covariance analysis of the speech signal to estimate the vocal tract's frequency response.
2. Solving the Yule-Walker equations to compute the LPCs, which describe how each sample depends on its predecessors.
3. Using the LPCs to synthesize speech by driving the all-pole filter with an excitation signal (e.g., a pulse train for voiced sounds or white noise for unvoiced sounds).

For example, in a simple LPC-based synthesizer, a recorded speech segment is analyzed to extract LPCs. During synthesis, these coefficients are applied to a filter, and an excitation signal (like a series of impulses for vowels) is passed through it to recreate a similar-sounding speech waveform.

In cloud-based speech synthesis systems, Tencent Cloud offers services like Tencent Cloud Text-to-Speech (TTS), which may leverage LPC or more advanced techniques (like neural vocoders) for high-quality voice generation. These services allow developers to integrate lifelike speech synthesis into applications efficiently.