In this paper, we study the role of long-time analytic phase of speech signals in spoken language recognition (SLR) and employ a set of features termed as instantaneous frequency cepstral coefficients (IFCC). We extract IFCC from long-time analytic phase, in an effort to capture long range acoustic features from speech signals. These features are used in combination with the traditional shifted delta cepstral coefficients (SDCC) for SLR. As the SDCC are extracted from spectral magnitude and IFCC are from analytic phase, they characterize long-time information of speech in different ways. The experiments conducted with NIST LRE 2017 task reveals the complementary effects of IFCC features to SDCC and deep bottleneck (DBN) features. The fusion of IFCC with SDCC/DBN features delivered relative improvements of 23.23% and 16.78% in average equal error rate over the SDCC and DBN features, respectively, indicating the benefits of information from analytic phase in SLR.

Decomposing speech signals into periodic and aperiodic components is an important task, finding applications in speech synthesis, coding, denoising, etc. In this paper, we construct a time-frequency coherence function to analyze spectro-temporal signatures of speech signals for distinguishing between deterministic and stochastic components of speech. The narrowband speech spectrogram is segmented into patches, which are represented as 2-D cosine carriers modulated in amplitude and frequency. Separation of carrier and amplitude/frequency modulations is achieved by 2-D demodulation using Riesz transform, which is the 2-D extension of Hilbert transform. The demodulated AM component reflects contributions of the vocal tract to spectrogram. The frequency modulated carrier (FM-carrier) signal exhibits properties of the excitation. The time-frequency coherence is defined with respect to FM-carrier and a coherence map is constructed, in which highly coherent regions represent nearly periodic and deterministic components of speech, whereas the incoherent regions correspond to unstructured components. The coherence map shows a clear distinction between deterministic and stochastic components in speech characterized by jitter, shimmer, lip radiation, type of excitation, etc. Binary masks prepared from the time-frequency coherence function are used for periodic-aperiodic decomposition of speech. Experimental results are presented to validate the efficiency of the proposed method.

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 all pass (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 residual based method using epochs, and better performance than the linear prediction PSOLA (LP-PSOLA).

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.

Identification of epochs from speech signals is a prominent task in speech processing. In this paper, epoch extraction is attempted from phase spectrum of speech signals. The phase spectrum of speech is modelled as an allpass (AP) filter by minimizing entropy of energy in the associated error signal. The AP residual thus obtained contains prominent unambiguous peaks at epoch locations. These peaks in AP residual constitute a set of candidate epoch locations from which appropriate ones are identified using a dynamic programming algorithm. The proposed method is evaluated on a subset of CMU Arctic database and it is observed that it delivered better epoch extraction performance than the prominent speech events estimation method-DYPSA. In case of telephone channel speech, the proposed method significantly outperformed zero frequency resonator based method also.

The objective of this work is to study the speaker-specific nature of analytic phase of speech signals. Since computation of analytic phase suffers from phase wrapping problem, we have used its derivative- The instantaneous frequency for feature extraction. The cepstral coefficients extracted from smoothed subband instantaneous frequencies (IFCC) are used as features for speaker verification. The performance of IFCC features is evaluated on NIST-2003 speaker recognition evaluation database and is compared with baseline mel-frequency cepstral coefficients (MFCC). The performance of IFCC features is observed to be comparable with MFCC features in terms of equal error rates and minimum detection cost function values. Different strategies for evaluating the speaker verification performance of IFCC and MFCC are explored and it is found that the evaluation based on cosine similarity delivers better performance than other strategies under consideration.