Discriminating simulated vocal tremor source using amplitude modulation spectra

Harmonics-to-noise ratio estimation with deterministically time-varying harmonic model for pathological voice signals

The harmonics-to-noise ratio (HNR) and other spectral noise parameters are important in clinical objective voice assessment as they could indicate the presence of nonharmonic phenomena, which are tied to the perception of hoarseness or breathiness. Existing HNR estimators are built on the voice signals to be nearly periodic (fixed over a short period), although voice pathology could induce involuntary slow modulation to void this assumption. This paper proposes the use of a deterministically time-varying harmonic model to improve the HNR measurements. To estimate the time-varying model, a two-stage iterative least squares algorithm is proposed to reduce model overfitting. The efficacy of the proposed HNR estimator is demonstrated with synthetic signals, simulated tremor signals, and recorded acoustic signals. Results indicate that the proposed algorithm can produce consistent HNR measures as the extent and rate of tremor are varied.

Automatic Classification of Healthy Subjects and Patients With Essential Vocal Tremor Using Probabilistic Source-Filter Model Based Noise Robust Pitch Estimation

Essential voice tremor (EVT) is a voice disorder resulting from dyscoordination within the laryngeal musculature. A low-frequency fluctuations of fundamental voice frequency or the strength of excitation amplitude is the main consequence of the disorder. The automatic classification of healthy control and EVT is useful tool for the clinicians. A typical automatic EVT classification involves three steps. The first step is to compute the pitch contour from the speech. The second step is to compute the features from the pitch contour, and the final step is to use a classifier to classify the features into healthy or EVT. It is shown that a high-resolution pitch contour estimated from the glottal closure instants (GCIs) is useful for EVT classification. The HPRC estimation can be very poor in the presence of noise. Hence, a probabilistic source filter model based noise robust GCI detection is used for HPRC estimation. The Empirical mode decomposition based feature extraction is used followed by a support vector machine classifier. The EVT classification performance is evaluated using recordings from 45 subjects. The proposed method is found to perform better than the baseline techniques in eight different additive noise conditions with six SNR levels.

Effects of maxillary expansion on hearing and voice function in non-cleft lip palate and cleft lip palate patients with transverse maxillary deficiency: a multicentric randomized controlled trial

INTRODUCTION

The association between the treatment of transverse maxillary deficiency and the recovery of hearing and voice functions has gained attention in recent years.

OBJECTIVE

This prospective controlled trial aimed to evaluate the effects of rapid maxillary expansion on hearing and voice function in children with non-cleft lip palate and bilateral cleft lip palate with transverse maxillary deficiency METHODS: 53 patients (26 non-cleft and 27 bilateral cleft lip palate; mean age, 11.1±1.8 years) requiring rapid maxillary expansion for correction of narrow maxillary arches were recruited for this trial. Eight sub-groups were established based on the degree of hearing loss. Pure-tone audiometric and tympanometric records were taken for each subject at four different time periods. The first records were taken before rapid maxillary expansion (T0), the second after expansion (T1) (mean, 0.8 months), the third after three months (T2) (mean, 3 months) and the fourth at the end of retention period (T3) (mean, 6 months). ANOVA and Tukey HSD post-hoc tests were used for data analysis. Additionally, voice analysis was done using an updated PRAAT software program in a computerized speech lab at T0 and T2. A paired-samplet-test was used for comparisons of mean values of T0 and T2 voice parameters within both groups.

RESULTS

Rapid maxillary expansion treatment produced a significant increase in the hearing levels and middle ear volumes of all non-cleft and bilateral cleft lip palate patients with normal hearing levels and with mild conductive hearing loss, during the T0-T1, T1-T2, T0-T2, and T0-T3 observation periods (p<0.05). The significant increase was observed in right middle ear volumes during the T0-T1, T0-T2 and T0-T3 periods in non-cleft patients with moderate hearing loss. For voice analysis, significant differences were observed only between the T0 and T2 mean fundamental frequency (F0) and jitter percentage (p<0.05) in the non-cleft group. In the cleft group, no significant differences were observed for any voice parameter between the T0 and T2 periods.

CONCLUSION

Correction of the palatal anatomy by rapid maxillary expansion therapy has a beneficial effect on both improvements in hearing and normal function of the middle ear in both non-cleft and bilateral cleft lip palate patients. Similarly, rapid maxillary expansion significantly influences voice quality in non-cleft patients, with no significant effect in BCLP patients.

Relationship between voice function and skeletal effects of rapid maxillary expansion

OBJECTIVE

To evaluate the effects of rapid maxillary expansion (RME) on the vocal quality, maxillary central incisors, midpalatal suture, and nasal cavity in patients with maxillary crossbite.

MATERIALS AND METHODS

Coronal CT scans of 30 subjects (14 boys, 16 girls; mean age, 12.01 ± 0.75) were taken before RME (T0), and at the end of the expansion phase (T1). Voice samples of all patients were recorded with a high-quality condenser microphone (RODE NT2-A) on a desktop computer at T0 and T1. Statistical analyses were performed using a paired-sample t-test. The degree of association between the changes in the voice parameters and nasal width was assessed with Pearson's correlation.

RESULTS

RME treatment produced a significant increase in the transverse dimensions of the midpalatal suture and nasal cavity between T0 and T1 ( P < .05). The maximum F0 and jitter (%) results were shown to decrease statistically significantly from T0 to T1 ( P < .001 and P = .042, respectively). Between T0 and T1, shimmer (%) and shimmer (dB) exhibited statistically significant increases ( P = .037 and P = .019, respectively).

CONCLUSIONS

After RME therapy, voice quality differences were found to be associated with increases in nasal width.

The effects of rapid maxillary expansion on voice function

OBJECTIVE

To evaluate the effects of rapid maxillary expansion (RME) on vocal function in patients with bilateral maxillary crossbite.

MATERIALS AND METHODS

We designed our research as a prospective, controlled, clinical study. The treatment group and the control group each had 20 subjects for a total of 40 subjects. Acoustic voice samples were recorded from all patients at T₁ and T₂ by the Multi-Dimensional Voice Program (MDVP Model 5105) for acoustic analysis in Computerized Speech Lab (CSL).

RESULTS

No statistically significant differences were found between the treatment and control groups in the means of any parameters.

CONCLUSIONS

RME does not change vocal quality or resonance, so it can be safely used with patients.

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.

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.