Further intelligibility results from human listening tests using the short-time phase spectrum

Alsteris, Leigh; Paliwal, Kuldip K.

doi:10.1016/j.specom.2005.10.005

Cited by 51 publications

(21 citation statements)

References 35 publications

(54 reference statements)

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“…Secondly, there has been a long standing belief among speech researchers that for small window durations (20-40 ms), 4 typically employed in speech processing, the short-time phase spectrum contains little useful information Oppenheim and Lim, 1981;Vary, 1985;Liu et al, 1997) and is unimportant for speech enhancement Wang and Lim, 1982). Recent subjective studies (Paliwal, 2003;Paliwal and Alsteris, 2003;Alsteris and Paliwal, 2004;Paliwal and Alsteris, 2005;Alsteris and Paliwal, 2006;Shi et al, 2006;Wó jcicki and Paliwal, 2007) suggest, however, that the phase spectrum does contain significant amount of useful information for speech intelligibility.…”

Section: Earlier Studies On the Usefulness Of The Short-time Phase Spmentioning

confidence: 99%

The importance of phase in speech enhancement

Paliwal

Wójcicki

Shannon

2011

Speech Communication

351

137

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Section: Earlier Studies On the Usefulness Of The Short-time Phase Spmentioning

confidence: 99%

The importance of phase in speech enhancement

Paliwal

Wójcicki

Shannon

2011

Speech Communication

351

137

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“…The access to extract features from phase spectrum were not as suitable as magnitude spectrum counterpart of a signal [5] and [6]. However, the phase characteristics of speech are also found to be significant as the magnitude for speaker characterization [7], [8]. Studies have been reported to use Fourier transform phase-based features, such as, Modified Group Delay (MGD) features [9] and [10], temporal modulation [11] to detect genuine vs. impostor speech.…”

Section: Introductionmentioning

confidence: 99%

Novel energy separation based instantaneous frequency features for spoof speech detection

Kamble

Patil

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Speech Synthesis (SS) and Voice Conversion (VC) presents a genuine risk of attacks for Automatic Speaker Verification (ASV) technology. In this paper, we evaluate front-end anti-spoofing technique to protect ASV system for SS and VC attack using a standard benchmarking database. In particular, we propose a novel feature set, namely, Energy Separation Algorithm-based Instantaneous Frequency Cosine Coefficients (ESA-IFCC) to detect the genuine and impostor speech. The experiments are carried out on ASV Spoof 2015 Challenge database. On the development set, the score-level fusion of proposed ESA-IFCC feature set with Mel Frequency Cepstral Coefficients (MFCC) gave an EER of 3.45 %, which reduced significantly from MFCC (6.98 %) and ESA-IFCC (5.43 %) with 13-D static features. The EER decreases further to 2.01 % and 1.89 % for ∆ and ∆∆ features derived from proposed ESA-IFCC features, respectively. The overall average error rate for known and unknown attacks in evaluation set was 6.79 % for ESA-IFCC and was significantly better than the MFCC (9.15 %) features.

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“…It was also observed that the phase spectral information helps in improving the accuracy of automatic speech recognizers. The perceptual importance of derivative of the phase spectrum, referred to as group delay (GD), was studied in [13]. It was reported that the phase spectrum plays a crucial role in human speech intelligibility under noisy conditions [14].…”

Section: B Significance Of Phase In Speech Perceptionmentioning

confidence: 99%

Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling

Vijayan¹,

Murty²

2015

IEEE/ACM Trans. Audio Speech Lang. Process.

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The phase spectrum of Fourier transform has received lesser prominence than its magnitude counterpart in speech processing. In this paper, we propose a method for parametric modeling of the phase spectrum, and discuss its applications in speech signal processing. The phase spectrum is modeled as the response of an allpass (AP) filter, whose coefficients are estimated from the knowledge of speech production process, especially the impulse-like nature of excitation source. A signal retaining only the phase spectral component of speech signal is derived by suppressing the magnitude spectral component, and is modeled as the output of an AP filter excited with a sequence of impulses. Entropy of energy of the input signal is minimized to estimate the coefficients of the AP filter. The resulting objective function, being nonconvex in nature, is minimized using particle swarm optimization. The group delay response of estimated AP filters can be used for accurate analysis of resonances of the vocal-tract system (VTS). The error signal associated with AP modeling provides unambiguous evidence about the instants of significant excitation of the VTS. The applications of the proposed AP modeling include, but not limited to, formant tracking, extraction of glottal closure instants, speaker verification and speech synthesis.

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Further intelligibility results from human listening tests using the short-time phase spectrum

Cited by 51 publications

References 35 publications

The importance of phase in speech enhancement

The importance of phase in speech enhancement

Novel energy separation based instantaneous frequency features for spoof speech detection

Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling

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