Significance of analytic phase of speech signals in speaker verification

Vijayan, Karthika; Reddy, Pappagari Raghavendra; Murty, K. Sri Rama

doi:10.1016/j.specom.2016.02.005

Cited by 48 publications

(34 citation statements)

References 49 publications

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“…The center frequencies of the bandpass filters are linearly-spaced and used to extract the component AM-FM signals of the speech segment and then determine the modulations around these center frequencies. Authors have chosen linearly-spaced filterbank for Butterworth filter as opposed to other frequency scale as Mel scale, Equivalent Rectangular Bandwidth (ERB) scale (this is in line with the recent finding reported in [14]). …”

Section: A Butterworth Filterbankmentioning

confidence: 74%

“…The IF in Fig. 3(c) is centered around 1500 Hz and shows spurious fluctuations on both side that makes it difficult to analyze and interpret the vocal tract system characteristics [14]. There could be two reasons for IF that has spurious fluctuations in a speech signal and they are:…”

Section: A Teager-kaiser Energy Operatormentioning

confidence: 99%

“…3(c), which is voiced regions have the changes in IF connected to impulse-like nature during speech production [14]. This is because the IF computed from the speech signal contains the information from both vocal tract system and excitation source.…”

Section: A Teager-kaiser Energy Operatormentioning

confidence: 99%

“…Temporal average is enumerated to obtain L-dimensional IF coefficients (IFCs) for each frame. Discrete Cosine Transform (DCT) is applied on IF to obtain stabilized cepstral features, i.e., proposed ESA-IFCC feature set [14]. Authors proposed to emphasize Fourier Transform (FT) phase information via IF computation using ESA, in ESA-IFCC.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: A Butterworth Filterbankmentioning

confidence: 74%

Section: A Teager-kaiser Energy Operatormentioning

confidence: 99%

Section: A Teager-kaiser Energy Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Many experts have done a lot in the corresponding studies [1][2][3] of speaker recognition. At present, the most popular features of speaker recognition are MFCC and LPCC [4][5][6]. In terms of recognition methods, vector quantization [7] , Gaussian Mixture Model(GMM) [7][8] and Hidden Markov Models(HMM) had gradually been applied in the field of speaker recognition.…”

Section: Introductionmentioning

confidence: 99%

Robust Speaker Recognition Algorithm

Hao¹,

Chen²,

Wang³

et al. 2017

Proceedings of the International Conference on Communication and Electronic Information Engineering (CEIE 2016)

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The accuracy of speaker recognition algorithm would be decreased greatly due to the noise issues. According to noisy environment, a new robust speaker recognition algorithm is proposed in this paper. After Mel-frequency Cepstral Coefficient (MFCC) feature extraction, the features are calibrated with half rised-sine function. Then the features are processed by feature normalization, feature folding and feature mapping. A method of combining BP neural network(NN) with Gaussian mixture model(GMM) is proposed to improve the recognition accuracy and robustness of the model. The neural network works in the probability space of GMM gathers the interactive information between different speakers. The experiment result proves that the proposed algorithm shows more accuracy and robustness.

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