Artificial Intelligence

Advances in Non-Linear Modeling for Speech Processing by Raghunath S. Holambe

By Raghunath S. Holambe

Advances in Non-Linear Modeling for Speech Processing comprises complicated subject matters in non-linear estimation and modeling strategies in addition to their purposes to speaker popularity.

Non-linear aeroacoustic modeling process is used to estimate the real fine-structure speech occasions, which aren't published through the quick time Fourier remodel (STFT). This aeroacostic modeling method offers the impetus for the excessive answer Teager power operator (TEO). This operator is characterised by means of a time solution that could song fast sign strength alterations inside a glottal cycle.

The cepstral beneficial properties like linear prediction cepstral coefficients (LPCC) and mel frequency cepstral coefficients (MFCC) are computed from the value spectrum of the speech body and the part spectra is ignored. to beat the matter of neglecting the part spectra, the speech construction procedure should be represented as an amplitude modulation-frequency modulation (AM-FM) version. To demodulate the speech sign, to estimation the amplitude envelope and on the spot frequency elements, the strength separation set of rules (ESA) and the Hilbert rework demodulation (HTD) set of rules are mentioned.

Different gains derived utilizing above non-linear modeling suggestions are used to strengthen a speaker id procedure. ultimately, it really is proven that, the fusion of speech creation and speech belief mechanisms can result in a strong function set.

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Additional resources for Advances in Non-Linear Modeling for Speech Processing (Springer Briefs in Electrical and Computer Engineering: Speech Technology)

Example text

The relationship between LPC and LPCC was originally derived by Atal in 1974 [13]. From the theoretical point of view, it is comparably easy to convert LP coefficients to LPCC, in the case of minimum-phase signals [1, 9]. 4 Time-Varying Linear Model The linear model defined in Eq. 2 can be rewritten for the output sequence o[n] as, K o[n] = φk o[n − k] + v[n]. , φ K ]T . This is a time-invariant linear model because the parameter vector θ is not a function of time, n. To turn this linear model into a time-varying one, we impose time dependence on θ , resulting in the time-varying LPC model in the form of K φk,n o[n − k] + v[n].

2 a FM signal and b it’s Teager energy In Fig. 2, a sample FM signal and it’s Teager energy is plotted. Here, the baseband signal is a pure sinusoidal signal. Notice that the output of the Teager Energy Operator in this case is a sinusoidal signal with the same frequency as the baseband signal. 1 Time Index Fig. 17) In Fig. 3, an AM–FM signal and it’s Teager energy is plotted. In this case, the operator’s tracking capabilities are not apparent, although we can spot some correlation between the peaks of the Teager energy and the zero-crossings of the AM–FM signal.

This is demonstrated in Fig. 2. The length of each segment needs to be approximately equal to the distance that sound travels in half a sample period [10, 11]. 11) P 2 fs √ where υc ≈ 20 Tυ ≈ 350 m/s is the speed of sound in the vocal tract, Tυ ≈ 305 K is the air temperature in the vocal tract and f s is the sampling frequency. This means that for a speech signal with f s = 16 kHz sampling frequency, we need P ≈ 2Lυcfs ≈ 15 tube segments to approximate the vocal tract. The objective of this analysis is to derive the transfer function V (z) between the glottal air-flow, u G (n) and the flow at the lips, u L (n).

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