2.2 Features for Stressed Speech
2.2.1 Feature Extraction
the data recording. The EEG signals were generated with 32 active AgCl electrodes and compressed using the sampling rate of 512 Hz. This database also consists of frontal face video recorded from 22 of the 32 participants. The F1-score values of 0.583, 0.563 and 0.502 were determined using the EEG signal for the classification of arousal, valence, like/dislike conditions, respectively.
2.2 Features for Stressed Speech
comprisingQfilters as follows, MFCC(i) =
XQ q=1
Yqcos
i(q− 1 2)π
20
, i= 1,2,· · ·, I (2.1) where, MFCC(i) represents theith,(1 ≤i≤I, I denotes the total number of cepstrum coefficients) cepstrum coefficient. The log-energy output of the qth filter is represented byYq. The bandwidth of each filter is derived on the Mel-scale to model the characteristics of human perception. Psychophys- ical studies show that, the human auditory system does not perceive the physical frequency of tone in linear scale [125]. It was observed that, the human perception of sound spectrum follows a Mel- scale. A Mel is a unit for the measurement of perceived frequency of a tone. The mapping of physical frequency on the Mel-scale is approximated as,
fMel = 2595 log(+fLinear/700) (2.2) where,fLinear andfMel represent the physical frequency and the Mel-frequency. The bandwidth of a critical band in Mel-scale varies linearly between 100 Hz to 1000 Hz of physical frequency and then in- creases logarithmically. It has been found that, the perception of particular frequency by the auditory system signifies the energy in a critical band around that frequency. Davis et al. [100] have studied the detail of cepstral features on the Mel-scale for the large vocabulary syllable-oriented continuous speech recognition system. MFCCs were noted to be very potent in retaining the significant acous- tic information in low frequency region and suppressing the insignificant spectral information in the higher frequency bands. The work reported in [126] illustrates that, the combination of static MFCCs and dynamic MFCCs in MFCCs features, which are determined using the first- and the second-order temporal derivatives of MFCCs, respectively, has resulted in very robust and informational for the development of speaker independent isolated word recognition system.
Teager Energy Operator (TEO) Autocorrelation Envelope Area (TEO-CB-Auto-Env): The vocal- tract system carries the essential information about the speech signal [1–3]. In the work reported in [44, 45], the detailed analysis on the modification in the vocal-tract system under stress condition is presented. Authors have found that, the air propagates within the vocal-tract system in a non- linear trend under adverse stressful condition. This non-linearity in the air flow was modeled using the Teager energy operator (TEO) and it reflects the variation in the airflow during the production
Discrete time impulse generator
Pitch frequency
Noise generator
Voiced/ unvoiced switch
Gain
H(z) = 1 1 +PP
p=1apz−p
All pole FIR filter
Estimated speech
Figure 2.1: Estimation of speech signal production using the linear prediction (LP) analysis.
of speech under stress condition. TEO-CB-Auto-Env features were extracted by filtering the speech signal through the Gabor bandpass filter. The critical band of each filter in filter-bank is determined by the precise partition of audio frequency range on the Mel-scale. After that, the TEO processing is performed over each filtered speech to obtain the TEO profile. The normalized autocorrelation enve- lope area corresponding to each TEO profile is considered as the TEO-CB-Auto-Env features. The experimental evaluations presented show that, TEO-CB-Auto-Env features capture the text indepen- dent information and model the nonlinear airflow, which cause listeners to perceive the information about the stress condition. These characteristics of TEO-CB-Auto-Env features increase both the accuracy and the reliability of tasks involving the classification and the recognition of stress classes.
Linear Prediction Coefficient (LPC): The earliest application of linear prediction (LP) for the anal- ysis of speech is studied by Atal and Hanauer [104, 105, 127]. The linear prediction analysis is a standard source-filter model based approach. In the source-filter model, all-pole finite impulse re- sponse (FIR) filter is used to model the slowly varying vocal-tract system. It is hypothesized that, the speech signal is produced by the convolution of fast varying excitation source and slowly varying vocal tract system. The estimation of speech samples using the LP analysis is depicted in the block diagram shown in Figure 2.1. The current samples(n)of speech signal can be predicted as the linear combination of pastP samples and it is computed as follows,
˜
s(n) =− XP
aps(n−p) (2.3)
2.2 Features for Stressed Speech
e(n) =s(n) + XP p=1
aps(n−p) (2.4)
The estimated speech sample is represented bys(n). The order of LP analysis is denoted by˜ P. The poles{ap}Pp=1of filter are used as the coefficients of linear combination and they are generally referred to as the linear prediction coefficients (LPCs).e(n)represents the residual error. LPCs are estimated my minimizing the instantaneous error between the current sample and the estimated sample. The LPC and the residual error model the significant characteristics of the vocal-tract system and the excitation source, respectively. As discussed earlier, speaker modifies the speech production system under stress condition. Therefore, the LP analysis leads to an effective approach for investigating the changes in the vocal-tract system and the excitation source under stress condition. Moreover, the linear prediction cepstral coefficients (LPCCs), which are derived from the LPC spectrum were reported to be effective for increasing the performances of speech recognition, speaker recognition, speaker verification and speaker identification tasks [101–105]. In addition to these characteristics, the LP analysis is widely used to estimate the pitch frequency from the residual error.
Perceptual Linear Prediction (PLP) Coefficient: The perceptual linear prediction (PLP) analysis is one of the commonly used technique to estimate the human auditory spectrum [107,108]. The PLP technique has exploited the three theory of psychophysics of hearing: the critical-band spectral res- olution, the equal-loudness curve and the intensity-loudness power law, respectively. The extraction process of PLP cepstral coefficients is described in the block diagram shown in Figure 2.2. At first, the power spectrum of framed speech signal is determined on the Bark-scale. After that, the auditory warped spectrum is convoluted with the power spectrum of the simulated critical band masking curve and down sampled to almost 1 Bark. In the next step, the power of each frequency bins are mapped according to the power law of Stevens by increasing power by 0.33 in magnitude [125]. The resulting auditory warped line spectrum is processed through the linear prediction analysis to obtain the LPC spectrum. Finally, cepstral coefficients are obtained by a recursion from the LPC spectrum and they are considered as PLP features. Hermansk [107] has observed that, the PLP model with5th order autoregressive model effectively reduces the speaker variability. These properties of PLP features were reported to be useful for developing the speaker independent ASR system. Moreover, the hu- man auditory system is less insensitive towards the slow and the fast varying frequency components in comparison to the average range of changes in the speech signals [128]. To model these charac-
Speech signal Short term processing (Framing)
Determination of power spectrum
Bark filter-bank
Equal-loudness pre-emphasis
Intensity=loudness(.)0.33
Linear prediction (LP) analysis
Recursive cepstrum computation
PLP cepstral coefficients
Figure 2.2: Estimation of perceptual linear prediction (PLP) coefficients.
teristics, PLP features are processed through the relative spectral (RASTA) technique and they are called as RASTA-PLP features [106]. The RASTA technique suppresses the slow and the fast vary- ing spectral components using the bandpass filter having a sharp spectral zero at the zero frequency.
RASTA-PLP features were noted to be robust in presence of convolutional and additive noises.
Filter-Bank Energy: The filter-bank analysis has become increasingly popular spectral analysis technique [1, 19, 109–111]. This technique represents the energy at each frequency band of interest in a speech signal using the highly overlapped bandpass filters. These bandpass filters approximate the frequency response of basilar membrane in the cochlea of human auditory system. The output of these filters are considered as the short time spectral envelops consisting of essential information
2.2 Features for Stressed Speech
of speech in different audio frequency bands. One of the conventional method for the computation of filter-bank energy employs the short term Fourier transform (STFT) followed by the low pass filtering technique [1]. In other work [126] , frequency bands are determined on the Mel-scale and energy in each frequency band is weighted and averaged for the precise representation of human hearing system. Furthermore, the combination of filter-bank analysis and linear prediction analysis have been also proposed [107]. In the work reported in [111], the discriminating ability of Mel-frequency cepstral coefficients (MFCCs) is improved by the weighted filter-bank analysis (WFBA). Climent et al. [110]
have investigated the designing of filters by combining the frequency filtering transformation and the time filtering transformation to develop the robust speech recognition system.