Influence of Upper Airway Surgery on Voice and Speech Recognition.

PURPOSE: Upper airway surgery comprises a set of techniques that modify the anatomy of the vocal tract, including tonsillectomy and septoplasty. The objective of this work is to study the changes in acoustic parameters and the effects on the identification or verification of the speaker through the speech produced after the vocal tract surgeries, comparing them with a control group. METHODS: A prospective study was performed between January 2019 and June 2019 including. The final study sample consisted of 84 patients who met the inclusion criteria. Of these, 31 underwent septoplasty, 26 tonsillectomy patients, and 27 controls. Demographic data and GRBAS evaluation were statistically evaluated. Tests were taken before surgery, 2 weeks after surgery and 3 months later. Furthermore, to establish the equal error rate, the recording of patients' voices was made with a succeeding acoustic analysis and programmed identification of the speaker through machine learning systems. RESULTS: A significant variance was observed in GRBAS, after surgery. Regarding acoustic parameters, a greater change was observed in the fundamental frequency at 2 weeks after surgery in the tonsillectomy group. Formants (F1-F3) and antiformants (AntiF1-AntiF3) changed in septoplasty group, not in tonsillectomy and control group at 3 months. When studying the impact of voice changes on the verification of the speaker through the speech, it was observed that there was a greater error in recognition in the tonsillectomy group at 2 weeks, coinciding with the results obtained in the rest of the parameters studied. CONCLUSIONS: Results suggest that upper airway surgery produces modifications in the vocal tract affecting GRBAS, acoustic parameters, including formants and antiformants, producing an effect on verification of the speaker through the speech.

Effect of Septoplasty On Voice Performance: Nasalance and Acoustic Study.

BACKGROUND: Septoplasty is a surgical technique for the correction of the nasal septum that may alter the vocal tract. The aim of our study is to assess whether this technique modifies nasalance and acoustic parameters, and their clinical implications in voice perception. METHODOLOGY: A prospective study was performed between January 2017 and June 2017 including 2 groups of patients: those undergoing septoplasty, and a control group. Subjective nasality questionnaire, objective nasalance with nasometer, and GRBAS (Grade, Roughness, Breathiness, Asthenia, Strain) assessment were statistically analysed. In addition, a recording of patients' voices was made with a subsequent acoustic analysis. Samples were taken: pre-surgically, 2 weeks after surgery and after 3 months. RESULTS: After septoplasty, a significant difference was observed in GRBAS, nasality questionnaire and nasometer nasalance, when compared with the control group. As for the acoustic analysis, no differences were observed in most parameters (F0, Jitter, Shimmer, HNR, NHR, Formants F1-F3), except for the antiF3 antiformant, which showed significant changes in all the vowels studied. CONCLUSIONS: Septoplasty can produce changes in the vocal tract, with an increase in initial nasalance but with subsequent normalization. Besides, minor changes were found in the acoustic analysis but with no clinical relevance.

Effect of Functional Endoscopic Sinus Surgery on Voice and Speech Recognition.

OBJECTIVE: Functional Endoscopic Sinus Surgery (FESS) is the surgery of choice for nasal polyposis and chronic rhinosinusitis. The aim of our study is to assess the influence of this surgery in the acoustic parameters of voice, and their implications in the systems of identification or verification of the speaker through the speech. MATERIAL AND METHODS: A prospective study was performed between January 2017 and June 2017 including two groups of patients: those undergoing FESS, and a control group. Demographic data and GRBAS assessment were statistically analyzed. In addition, a recording of patients' voices was made with a subsequent acoustic analysis and automatic identification of the speaker through machine learning systems, establishing the equal error rate. Samples were taken before surgery, 2 weeks after surgery and 3 months later. RESULTS: After FESS, a significant difference was observed in Grade, Roughness, Breathiness, Asthenia, Strain (GRBAS). Besides, acoustic analysis showed a significance decrease in fundamental frequency (F0), when compared with the control group. For the automatic identification of the speaker through computer systems, we found that the equal error rate is higher in the FESS group. CONCLUSIONS: Results suggest that FESS produce a decrease of F0 and changes in the vocal tract that derive in an increase in the error of recognition of the speaker in FESS patients.

Wavelet packets feasibility study for the design of an ECG compressor.

Most of the recent electrocardiogram (ECG) compression approaches developed with the wavelet transform are implemented using the discrete wavelet transform. Conversely, wavelet packets (WP) are not extensively used, although they are an adaptive decomposition for representing signals. In this paper, we present a thresholding-based method to encode ECG signals using WP. The design of the compressor has been carried out according to two main goals: (1) The scheme should be simple to allow real-time implementation; (2) quality, i.e., the reconstructed signal should be as similar as possible to the original signal. The proposed scheme is versatile as far as neither QRS detection nor a priori signal information is required. As such, it can thus be applied to any ECG. Results show that WP perform efficiently and can now be considered as an alternative in ECG compression applications.

Glottal Gap tracking by a continuous background modeling using inpainting.

The visual examination of the vibration patterns of the vocal folds is an essential method to understand the phonation process and diagnose voice disorders. However, a detailed analysis of the phonation based on this technique requires a manual or a semi-automatic segmentation of the glottal area, which is difficult and time consuming. The present work presents a cuasi-automatic framework to accurately segment the glottal area introducing several techniques not explored before in the state of the art. The method takes advantage of the possibility of a minimal user intervention for those cases where the automatic computation fails. The presented method shows a reliable delimitation of the glottal gap, achieving an average improvement of 13 and 18% with respect to two other approaches found in the literature, while reducing the error of wrong detection of total closure instants. Additionally, the results suggest that the set of validation guidelines proposed can be used to standardize the criteria of accuracy and efficiency of the segmentation algorithms.

Dimensionality reduction of a pathological voice quality assessment system based on Gaussian mixture models and short-term cepstral parameters.

Voice diseases have been increasing dramatically in recent times due mainly to unhealthy social habits and voice abuse. These diseases must be diagnosed and treated at an early stage, especially in the case of larynx cancer. It is widely recognized that vocal and voice diseases do not necessarily cause changes in voice quality as perceived by a listener. Acoustic analysis could be a useful tool to diagnose this type of disease. Preliminary research has shown that the detection of voice alterations can be carried out by means of Gaussian mixture models and short-term mel cepstral parameters complemented by frame energy together with first and second derivatives. This paper, using the F-Ratio and Fisher's discriminant ratio, will demonstrate that the detection of voice impairments can be performed using both mel cesptral vectors and their first derivative, ignoring the second derivative.

Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics.

There exist many acoustic parameters employed for pathological assessment tasks, which have served as tools for clinicians to distinguish between normophonic and pathological voices. However, many of these parameters require an appropriate tuning in order to maximize its efficiency. In this work, a group of new and already proposed modulation spectrum (MS) metrics are optimized considering different time and frequency ranges pursuing the maximization of efficiency for the detection of pathological voices. The optimization of the metrics is performed simultaneously in two different voice databases in order to identify what tuning ranges produce a better generalization. The experiments were cross-validated so as to ensure the validity of the results. A third database is used to test the optimized metrics. In spite of some differences, results indicate that the behavior of the metrics in the optimization process follows similar tendencies for the tuning databases, confirming the generalization capabilities of the proposed MS metrics. In addition, the tuning process reveals which bands of the modulation spectra have relevant information for each metric, which has a physical interpretation respecting the phonatory system. Efficiency values up to 90.6% are obtained in one tuning database, while in the other, the maximum efficiency reaches 71.1%. Obtained results also evidence a separability between normophonic and pathological states using the proposed metrics, which can be exploited for voice pathology detection or assessment.

Corrigendum: Voice Pathology Detection Using Modulation Spectrum-Optimized Metrics.

[This corrects the article on p. 1 in vol. 4, PMID: 26835449.].

Modulation Spectra Morphological Parameters: A New Method to Assess Voice Pathologies according to the GRBAS Scale.

Disordered voices are frequently assessed by speech pathologists using perceptual evaluations. This might lead to problems caused by the subjective nature of the process and due to the influence of external factors which compromise the quality of the assessment. In order to increase the reliability of the evaluations, the design of automatic evaluation systems is desirable. With that in mind, this paper presents an automatic system which assesses the Grade and Roughness level of the speech according to the GRBAS perceptual scale. Two parameterization methods are used: one based on the classic Mel-Frequency Cepstral Coefficients, which has already been used successfully in previous works, and other derived from modulation spectra. For the latter, a new group of parameters has been proposed, named Modulation Spectra Morphological Parameters: MSC, DRB, LMR, MSH, MSW, CIL, PALA, and RALA. In methodology, PCA and LDA are employed to reduce the dimensionality of feature space, and GMM classifiers to evaluate the ability of the proposed features on distinguishing the different levels. Efficiencies of 81.6% and 84.7% are obtained for Grade and Roughness, respectively, using modulation spectra parameters, while MFCCs performed 80.5% and 77.7%. The obtained results suggest the usefulness of the proposed Modulation Spectra Morphological Parameters for automatic evaluation of Grade and Roughness in the speech.

Characterization of dysphonic voices by means of a filterbank-based spectral analysis: sustained vowels and running speech.

OBJECTIVES: This article presents a comparative study of the spectral power distribution for normal and dysphonic voices, both for sustained vowels and running speech. The objective of this study was to find robust cues of dysphonia in spectral domain. For this purpose, recordings from two databases are processed, one of them including both sustained vowels and running speech. Additionally, a new measure of stability is introduced (decorrelation time). The application of this measure to the power spectrum is also tested as a cue of dysphonia. MATERIALS AND METHODS: The spectral analysis is done having both an auditory model and the filterbank approach as references to the computation of discrete spectrograms. Results are obtained from three sets of recordings belonging to two different databases. RESULTS: The reported results indicate that only minor differences exist in the shape of the power spectrum of normal and dysphonic voices when performing sustained vowel phonation tasks. However, the calculated band power decorrelation times indicate that power in bands between 2000 and 6400Hz is significantly less stable in dysphonic voices. As for running speech, the stability of spectral power is not such a good indicator of dysphonia, but there is a significant difference between normal and dysphonic voices in the power level of high-frequency bands (above 5300Hz). In addition, this means that sampling rates above 10.6ksps are needed for assessing running speech in spectral domain. Also, the results involving decorrelation times indicate that for short-time spectral analysis, frame rates above 100 frames/s should be preferred.

Pathological likelihood index as a measurement of the degree of voice normality and perceived hoarseness.

A new index is introduced in this article to measure the degree of normality in the speech. The proposed parameter has demonstrated to be correlated with the perceived hoarseness, giving an indication of the degree of normality. The calculation of such a parameter is based on a statistical model developed to represent normal and pathological voices. The modeling is built around Gaussian mixture models and Mel frequency cepstral coefficients. The proposed index has been named pathological likelihood index (PLI). PLI is compared with other aperiodicity features (such as jitter and shimmer), and measurements sensitive to additive noise (such as harmonics-to-noise ratio (HNR), cepstrum-based HNR, normalized noise energy, and glottal-to-noise excitation ratio). The proposed parameter is revealed to be a good estimator of the presence of pathology, showing lower correlation with noise, frequency, and amplitude perturbation parameters than these classical features among them.

Nonlinear trend estimation of the ventricular repolarization segment for T-wave alternans detection.

Repolarization alternans or T-wave alternans (TWA) is a subject of great interest as it has been shown as a risk stratifier for sudden cardiac death. As TWA consists of subtle and nonvisible variations of the ST-T complex, its detection may become more difficult in noisy environments, such as stress testing or Holter recordings. In this paper, a technique based on the empirical-mode decomposition (EMD) to separate the useful information of the ST-T complex from noise and artifacts is proposed. The identification of the useful part of the signal is based on the study of complexity in the EMD domain by means of the Hjorth descriptors. As a result, a robust technique to extract the trend of the ST-T complex has been achieved. The evaluation of the method is carried out with the spectral method (SM) over several public domain databases with ECGs sampled at different frequencies. The results show that the SM with the proposed technique outperforms the traditional SM by more than 2 dB. Also, the robustness of this technique is guaranteed as it does not introduce any additional distortion to the detector in noiseless conditions.

The effectiveness of the glottal to noise excitation ratio for the screening of voice disorders.

This paper evaluates the capabilities of the Glottal to Noise Excitation Ratio for the screening of voice disorders. A lot of effort has been made using this parameter to evaluate voice quality, but there do not exist any studies that evaluate the discrimination capabilities of this acoustic parameter to classify between normal and pathological voices, and neither are there any previous studies that reflect the normative values that could be used for screening purposes. A set of 226 speakers (53 normal and 173 pathological) taken from a voice disorders database were used to evaluate the usefulness of this parameter for discriminating normal and pathological voices. To evaluate this parameter, the effect of the bandwidth of the Hilbert envelopes and the frequency shift have been analyzed, concluding that a good discrimination is obtained with a bandwidth of 1000 Hz and a frequency shift of 300 Hz. The results confirm that the Glottal to Noise Excitation Ratio provides reliable measurements in terms of discrimination among normal and pathological voices, comparable to other classical long-term noise measurements found in the literature, such as Normalized Noise Energy or Harmonics to Noise Ratio, so this parameter can be considered a good choice for screening purposes.

Acoustic analysis of voice using WPCVox: a comparative study with Multi Dimensional Voice Program.

In this study, two different tools developed for the parametric extraction and acoustic analysis of voice samples are compared. The main goal of the paper is to contrast the results obtained using the classical Multi Dimensional Voice Program (MDVP), with the results obtained with the novel WPCVox. The aim of this comparison was to find differences and similarities in the parameters extracted with both systems in order to make comparison of measurements and data transfer among both equipments. The study was carried out in two stages: in the first, a wide sample of healthy voices belonging to Spanish-speaking adults from both genders were used to carry out a direct comparison between the results given by MDVP and those obtained with WPCVox. In the second stage, a sample of 200 speakers (53 normal and 173 pathological) taken from a commercially available database of voice disorders were used to demonstrate the usefulness of WPCVox for the acoustic analysis and the characterization of normal and pathological voices. The results conclude that WPCVox provides very reliable measurements which are very similar to those obtained using MDVP, and very similar capabilities to discriminate among normal and pathological voices.