Vocal Tract Response Research Articles

Multi-Dimensional Spectral Process for Cepstral Feature Engineering & Formant Coding

The fundamental frequency feature is essential for Automatic Speech Recognition because its patterns convey a paralanguage and its tuning normalizes other speech features. Human speech is multidimensional because it is minimally represented by three variables: the intonation (or pitch), the formants (or timbre), and the speech resolution (or depth). These variables represent the hidden states of the local glottal variation, the vocal tract response, and the frequency scale, respectively. Computing them one by one is not as efficient as computing them together, so this article introduces a new speech feature extraction approach. The article is introductory; it focuses on the basic concepts of our new approach and does not elaborate on all applications. It demonstrates that the unit of a cepstral value, which is a spectral value of spectrums, is a unit of acceleration since its discrete variable, the quefrency, can be expressed in Hertz-per-microsecond. The article shows how to produce refined voice analysis from robust estimates and how to reconstruct speech signals from feature spaces. And it concludes that the pitch track of the new approach is as good as two open-source pitch extractors. Combining multiple processes, attenuating background noises, and enabling distant-speech recognition, we introduce the Speech Quefrency Transform (SQT) approach as well as multiple quefrency scales. SQT is a set of frequency transforms whose spectral leakages are controlled per a frequency-modulation model. SQT captures the stationarity of time series onto a hyperspace that resembles the cepstrogram when it is reduced for pitch track extraction.

A Robust Speech Features Extractor & Reconstructor For Artificial Intelligence Frontends

Human speech consists mainly of three components: a glottal signal, a vocal tract response, and a harmonic shift. The three respectively correlate with the intonation (pitch), the formants (timbre), and the speech resolution (depth). Adding the intonation of the Fundamental Frequency (FF) to Automatic Speech Recognition (ASR) systems is necessary. First, the intonation conveys a primitive paralanguage. Second, its speaker-tuning reduces background noises to clarify acoustic observations. Third, extracting the speech features is more efficient when they are computed together at the same time. This work introduces a frequency-modulation model, a novel quefrency-based speech feature extraction that is named Speech Quefrency Transform (SQT), and its proper quefrency scaling and transformation function. The cepstrums, which are spectrums of spectrums, are suggested in time unit accelerations, whereby the discrete variable, the quefrency, is measured in Hertz-per-microsecond. The extracted features are comparable to Mel-Frequency Cepstral Coefficients (MFCC) integrated within a quefrency-based pitch tracker. The SQT transform directly expands time samples of stationary signals (i.e., speech) to a higher dimensional space, which can help generative Artificial Neural Networks (ANNs) in unsupervised Machine Learning and Natural Language Processing (NLP) tasks. The proposed methodologies, which are a scalable solution that is compatible with dynamic and parallel programming for refined speech and cepstral analysis, can robustly estimate the features after applying a matrix multiplication in less than a hundred sub-bands, preserving precious computational resources.

Effects of Nasalization on Vocal Tract Response Curve

Earlier studies have shown that nasalization affects the radiated spectrum by modifying the vocal tract transfer function in a complex manner. Here we study this phenomenon by measuring sine-sweep response of 3-D models of the vowels /u, a, ᴂ, i/, derived from volumetric MR imaging, coupled by means of tubes of different lengths and diameters to a 3-D model of a nasal tract. The coupling introduced a dip into the vocal tract transfer function. The dip frequency was close to the main resonance of the nasal tract, a result in agreement with the Fujimura & Lindqvist in vivo sweep tone measurements [Fujimura & Lindqvist, 1972]. With increasing size of the coupling tube the depth of the dip increased and the first formant peak either changed in frequency or was split by the dip. Only marginal effects were observed of the paranasal sinuses. For certain coupling tube sizes, the spectrum balance was changed, boosting the formant peaks in the 2 - 4 kHz range. A velopharyngeal opening introduces a dip in the transfer function at the main resonance of the nasal tract. Its depth increases with the area of the opening and its frequency rises in some vowels.

High Security and Capacity of Image Steganography for Hiding Human Speech Based on Spatial and Cepstral Domains

A new technique of hiding a speech signal clip inside a digital color image is proposed in this paper to improve steganography security and loading capacity. The suggested technique of image steganography is achieved using both spatial and cepstral domains, where the Mel-frequency cepstral coefficients (MFCCs) are adopted, as very efficient features of the speech signal. The presented technique in this paper contributes to improving the image steganography features through two approaches. First is to support the hiding capacity by the usage of the extracted MFCCs features and pitches extracted from the speech signal and embed them inside the cover color image rather than directly hiding the whole samples of the digitized speech signal. Second is to improve the data security by hiding the secret data (MFCCs features) anywhere in the host image rather than directly using the least significant bits substitution of the cover image. At the recovering side, the proposed approach recovers these hidden features and using them to reconstruct the speech waveform again by inverting the steps of MFCCs extraction to recover an approximated vocal tract response and combine it with recovered pitch based excitation signal. The results show a peak signal to noise ratio of 52.4 dB of the stego-image, which reflect a very good quality and a reduction ratio of embedded data to about (6%–25%). In addition, the results show a speech reconstruction degree of about 94.24% correlation with the original speech signal.

Demodulation of Narrowband Speech Spectrograms Using the Riesz Transform

We propose a two-dimensional (2-D) multicomponent amplitude-modulation, frequency-modulation (AM-FM) model for a spectrogram patch corresponding to voiced speech, and develop a new demodulation algorithm to effectively separate the AM, which is related to the vocal tract response, and the carrier, which is related to the excitation. The demodulation algorithm is based on the Riesz transform and is developed along the lines of Hilbert-transform-based demodulation for 1-D AM-FM signals. We compare the performance of the Riesz transform technique with that of the sinusoidal demodulation technique on real speech data. Experimental results show that the Riesz-transform-based demodulation technique represents spectrogram patches accurately. The spectrograms reconstructed from the demodulated AM and carrier are inverted and the corresponding speech signal is synthesized. The signal-to-noise ratio (SNR) of the reconstructed speech signal, with respect to clean speech, was found to be 2 to 4 dB higher in case of the Riesz transform technique than the sinusoidal demodulation technique.

Algorithm for Cepstral Analysis and Homomorphic Filtering for Glottal Feature Estimation in Speech Signals

Cepstral analysis and homomorphic filtering, are widely used tools for speech analysis. This paper presents an algorithm for cepstral analysis of speech signals. Homomorphic speech processing and cepstrum estimation is performed using the fast Fourier transform (FFT), its inverse operation (IFFT) and the logarithmic magnitude, thus the estimation of cepstral parameters is reached. Using these characteristics, the calculation of glottal speech features is completed, using homomorphic filtering to separate the source and filter signals from the vocal tract response. The last part of this paper shows the obtained signals in the processing task and the analysis as a result of the algorithm process.

Glottal source processing: From analysis to applications

The great majority of current voice technology applications rely on acoustic features, such as the widely used MFCC or LP parameters, which characterize the vocal tract response. Nonetheless, the major source of excitation, namely the glottal flow, is expected to convey useful complementary information. The glottal flow is the airflow passing through the vocal folds at the glottis. Unfortunately, glottal flow analysis from speech recordings requires specific and complex processing operations, which explains why it has been generally avoided. This paper gives a comprehensive overview of techniques for glottal source processing. Starting from analysis tools for pitch tracking, detection of glottal closure instant, estimation and modeling of glottal flow, this paper discusses how these tools and techniques might be properly integrated in various voice technology applications.

Cepstral and linear prediction techniques for improving intelligibility and audibility of impaired speech

Human speech becomes impaired i.e., unintelligible due to a variety of reasons that can be either neurological or anatomical. The objective of the research was to improve the intelligibility and audibility of the impaired speech that resulted from a disabled human speech mechanism with impairment in the acoustic system-the supra-laryngeal vocal tract. For this purpose three methods are presented in this paper. Method 1 was to develop an inverse model of the speech degradation using the Cepstral technique. Method 2 was to replace the degraded vocal tract response by a normal vocal tract response using the Cepstral technique. Method 3 was to replace the degraded vocal tract response by a normal vocal tract response using the Linear Prediction technique.

Voice Production Mechanisms of Vocal Vibrato in Male Singers

Vibrato constitutes one of the most interesting features of singing voice. It has been the topic of interest of many researchers in different knowledge areas such as acoustics, physiology, and psychoacoustics. In spite of this, some basic aspects related to vibrato production still remain unclear, such as the origin of its most relevant acoustical features, (frequency and amplitude variations of the partials) or the relevance of recently identified physiological effects, such as pitch synchronous variations of the voice organ. In this paper, a novel interactive vibrato production model will be proposed in order to accomplish a realistic voice production description, in such a way that the source-tract interaction will be included in the prediction model. This will allow us to identify, from the acoustical signal, additional effects typical of vocal vibrato, such as slight pitch synchronous variations of the vocal tract response, as well as to analyze the relationship between the acoustical features of vibrato and the voice production elements

Effect of the glottal source and the vocal tract on the partials amplitude of vibrato in male voices

In this paper the production of vocal vibrato is investigated. The most relevant features of the acoustical vibrato signal, frequency and amplitude variations of the partials, will be related to the voice production features, glottal source (GS) and vocal tract response (VTR). Unlike previous related works, in this approach, the effect on the amplitude variations of the partials of each one of the above-mentioned voice production features will be identified in recordings of natural singing voice. Moreover, we will take special care of the reliability of the measurements, and, to this aim, a noninteractive vibrato production model will be also proposed in order to describe the vibrato production process and, more importantly, validate the measurements carried out in natural vibrato. Based on this study, it will be shown that during a few vibrato cycles, the glottal pulse characteristics, as well as the VTR, do not significantly change, and only the fundamental frequency of the GS varies. As a result, the pitch variations can be attributed to the GS, and these variations, along with the vocal tract filtering effect, will result in frequency and amplitude variations of the acoustic signal partials.

Vocal-tract computation: How to make it robust and fast

Numerical methods are described to speed up the frequency-domain calculation of the vocal-tract response and effectively determine the associated pole/zero patterns. These methods include algorithms to properly decompose the numerator and the denominator of the transfer function, to estimate formant patterns of a lossy vocal-tract system from its lossless counterpart by means of linear interpolation, to recover a heavily damped spectral peak by removing its neighboring formant from the true transfer function, and to utilize the information of residues at the system poles to ensure that no pole/zero is missed.

Vocal tract response to toxic injury: Clinical issues

Toxic substances commonly affect the voice. In many cases, the mechanism of toxicity is unknown. Treatment is often based on intuition, rather than scientific fact. Extensive collaborative research is needed.

Glottal inverse filtering by joint estimation of an AR system with a linear input model

Glottal inverse filtering is a technique by which the flow past the time varying glottal constriction is estimated by a filtering operation on the acoustic signal in human speech. The filtering operation removes the effects of the vocal tract resonances to reveal the underlying voice source signal. A linear model of a glottal pulse waveform is described along with a procedure for jointly determining an AR model of the vocal tract response together with the parameters of the glottal pulse model. This technique is applied to inverse filtering in human speech and the results are compared to covariance method LPC analysis.

Automatic glottal inverse filtering from speech and electroglottographic signals

The flow of air through the glottis, the glottal volume-velocity, reflects the action of the vocal folds and is thus an important indicator of laryngeal function. However, it cannot be measured directly because of vocal tract filtering. We have developed an automated on-line method to determine the glottal volume-velocity waveform from normal and pathological speech based on digital inverse filtering. The method developed addresses the problems of accurate identification of vocal tract parameters and reduction of low-frequency noise. The vocal tract filter is estimated by analysis of the undriven vocal tract response during closed glottis, as identified from an electroglottographic signal. Low-frequency noise is attenuated by a high-pass filtering operation followed by a low-pass compensation. The complete inverse filtering method provides reliable glottal volume-velocity waveforms for both normal and pathological speech.

Formant frequencies, bandwidths, and Qs in helium speech.

We used linear prediction analysis to estimate a diver's vocal tract response for isolated vowels spoken in air at 1 atm and in heliox at simulated depths of 54-, 120-, 300-, and 500-m seawater. We specifically measured formant frequency, bandwidth, and Q changes as a function of environment. The formant frequencies shifted upward nonlinearly in helium speech. The formant bandwidths in helium speech increased as much as 14 times their corresponding bandwidths in normal speech. The ratios of formant Qs (helium speech to normal speech) ranges from 0.3 (low formants) to 2 (high formants).