Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling

Vijayan, Karthika; Murty, K. Sri Rama

doi:10.1109/taslp.2015.2479045

Cited by 17 publications

(6 citation statements)

References 63 publications

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“…To reduce the noise and to estimate the errors as [5], Log has been taken for each frame power spectrum values.…”

Section: Figure 2: Triangular Band Pass Filter Bankmentioning

confidence: 99%

Dysfluency Recognition by using Spectral Entropy Features

Vinay¹,

Bharathi²

2019

IJEAT

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In recent decades, speech recognition technology has improved effectively and significantly, but it is restricted only for a stream of words. These recognition systems assist human’s effectively for structured speech but for unstructured speech this assistance is not so effective for humans to communicate with machines, because unstructured stream of word lacks in providing useful information about pronunciation and punctuation. Recovering of such structural information by detecting the position of each phones in a sentence by locating the sentence boundaries, repeated words and missing phones in each phrase. The proposed work investigates the spectral entropy features, for the automatic detection of voiced and non-voiced regions, in the process of dysfluent speech recognition. The entropy features are estimated by normalizing the Fourier transform spectrum as Probability mass function (PMF). For clear formants of speech, the value of entropy is low and the value of entropy is high for flat distribution of silence part or if there is any noise in speech sample. A comparison of entropy features with Word Error Rate is presented in the proposed work

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“…To reduce the noise and to estimate the errors as [5], Log has been taken for each frame power spectrum values.…”

Section: Figure 2: Triangular Band Pass Filter Bankmentioning

confidence: 99%

Dysfluency Recognition by using Spectral Entropy Features

Vinay¹,

Bharathi²

2019

IJEAT

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“…The AP modeling strategy for phase spectrum by entropy minimization was proposed in [27]. In this work, we use the gradient descent algorithm with appropriately small step size, to minimize the entropy function J(w) to obtain the APCs w [27]. The group delay response of the estimated AP filter, for a short-time segment of speech is shown in Figure 1(d).…”

Section: Allpass Modeling Of Phase Spectrummentioning

confidence: 99%

“…It can be noticed that the peaks in group delay response coincide with the peaks in LP spectrum in Figure 1(b), demonstrating information about VTS resonances. The resultant AP residual is obtained by noncausal inverse filtering of LP residual y[n] through estimated AP filter H(z) [27], and is shown in Figure 2(c). The AP residual demonstrates unambiguous peaks at epochs, as opposed to multiple bipolar peaks around epochs in LP residual due to the presence of phase spectrum, as shown in Figure 2(b).…”

Section: Allpass Modeling Of Phase Spectrummentioning

confidence: 99%

Prosody Modification Using Allpass Residual of Speech Signals

Vijayan¹,

Murty²

2016

Interspeech 2016

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In this paper, we attempt to signify the role of phase spectrum of speech signals in acquiring an accurate estimate of excitation source for prosody modification. The phase spectrum is parametrically modeled as the response of an allpass (AP) filter, and the filter coefficients are estimated by considering the linear prediction (LP) residual as the output of the AP filter. The resultant residual signal, namely AP residual, exhibits unambiguous peaks corresponding to epochs, which are chosen as pitch markers for prosody modification. This strategy efficiently removes ambiguities associated with pitch marking, required for pitch synchronous overlap-add (PSOLA) method. The prosody modification using AP residual is advantageous than time domain PSOLA (TD-PSOLA) using speech signals, as it offers fewer distortions due to its flat magnitude spectrum. Windowing centered around unambiguous peaks in AP residual is used for segmentation, followed by pitch/duration modification of AP residual by mapping of pitch markers. The modified speech signal is obtained from modified AP residual using synthesis filters. The mean opinion scores are used for performance evaluation of the proposed method, and it is observed that the AP residual-based method delivers equivalent performance as that of LP residualbased method using epochs, and better performance than the linear prediction PSOLA (LP-PSOLA).

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“…Even though actually the convolution of the time domain signal is same as the addition in the phase domain (Loweimi et al, 2015;Vijayan and Murty, 2015) signal, which may be useful for carrying out deconvolution, however, there is no direct phase modeling algorithm available for such decomposition. Thus developing a fundamental phase based model of source-filter in a trustworthy way elevates the PS to be used in speech analysis practically.…”

Section: Introductionmentioning

confidence: 99%

Speech Signal Analysis in Phase Domain

Chowdhury¹,

Rahman²

2020

Journal of Computer Science

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Speech signal analysis based on the Phase Spectrum (PS) in the recent years becomes more popular to the researchers due to its attractive properties. Though some problem arises during its processing but appropriate modification gives the useful result. In this study, we proposed a new method of speech signal analysis based on its phase spectral representation. After analyzing the PS we modified it vocal tract dominated spectrum by utilizing the minimum-phase stable component of the speech spectrum. The modified signal holding only the phase spectral component is used for the parametric modeling of the speech signal to estimate the characteristics of vocal tract perfectly. This extracted feature was found to provide complementary evidence from the magnitude spectrum of speech but with better resolution. According to perceptual analysis, the PS takes precedence over the magnitude spectrum. This research utilizes the Group Delay (GD) spectrum as a representative of the PS because of its meaningful characteristic. All pole modeling is performed on the signal evaluated from the GD spectrum to find the resonances of the vocal tract system accurately. The effectiveness of the method is tested by synthesizing some vowels over a range of pitch periods from low to high pitched speech. The validity of the proposed method is also verified by plotting the formant contour on the spectrogram of a sentence from the TIMIT database and standard F2-F1 plot of natural speech spoken by male and female speakers. The proposed method performs better than the state-of-the-art methods.

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Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling

Cited by 17 publications

References 63 publications

Dysfluency Recognition by using Spectral Entropy Features

Dysfluency Recognition by using Spectral Entropy Features

Prosody Modification Using Allpass Residual of Speech Signals

Speech Signal Analysis in Phase Domain

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