Voice disorder discrimination using vowel acoustic measures in female speakers.

BACKGROUND: Sustained vowels are important vocal tasks that have been investigated in discriminating voice disorders using acoustic analysis. To date, no study has combined vowel acoustic measures only that evaluate major aspects of the pathological voice signals in voice disorder discrimination. AIMS: To investigate the value of vowel acoustic measures that quantify glottal noise, signal stability, signal periodicity, spectral slope and overall voice quality in discriminating female speakers with and without voice disorders. METHODS & PROCEDURES: Sustained vowel /ɑ/ samples were extracted from 133 voice-disordered female patients and 97 non-voice disordered female speakers and were signal typed prior to analysis. Praat software was used to measure harmonics-to-noise ratio (HNR), glottal-to-noise excitation ratio (GNE), the standard deviation of fundamental frequency (F0SD) and cepstral peak prominence (CPPp); and the Analysis of Dysphonia in Speech and Voice (ADSV) program was used to measure CPPadsv, low/high spectral ratio (LH) and the cepstral/spectral index of dysphonia (CSID). Outcome measures included sensitivity, specificity, and discrimination accuracy. OUTCOMES & RESULTS: As individual acoustic measures, only spectral-based measures showed good (CPPadsv) and acceptable (CSID) discrimination results. The HNR, GNE and CPPp measures had acceptable sensitivity but poor or non-acceptable specificity and discrimination accuracy. Logistic regression models with all Praat measures (F0SD, HNR, GNE, CPPp) plus ADSV measures (CPPadsv, LH or CSID) provided excellent sensitivity, good-to-excellent specificity and excellent discrimination accuracy. ROC analysis for all individual measures showed that CPPadsv, CSID, CPPp, GNE and F0SD had the highest area under the curve (AUC) values. CONCLUSIONS & IMPLICATIONS: A combination of acoustic measures that evaluate the major aspects of vocal dysfunction resulted in good to excellent voice discrimination outcomes. Individual acoustic measures had lower discrimination ability than combined measures. The findings implied that acoustic measures extracted from a prolonged vowel were useful in voice disorder discrimination. WHAT THIS PAPER ADDS: What is already known on this subject Acoustic measures hold great value in discriminating voice disorders from normal voices. However, no study has evaluated discrimination values of a combination of sustained vowel acoustic measures that quantify additive noise, signal stability, signal periodicity, spectral slope and overall voice quality in single-gender cohorts. Previous studies have not used signal typing (the classification of the acoustic signals) for time-based measures, impacting the reliability of discrimination. What this study adds to the existing knowledge This study was the first to implement signal typing to include sustained vowel samples of Types 1 and 2 signals for discrimination statistics. We showed that a combination of vocal acoustic measures using time- and spectral-based extraction from the sustained /ɑ/ vowel evaluating additive noise, signal stability, signal periodicity, spectral slope and overall voice quality resulted in good to excellent sensitivity, specificity and discrimination accuracy. As individual measures, traditional time-based measures such as HNR had rather limited discrimination values whilst spectral-based measures provided higher discrimination values. Measures that are sensitive to signal types have low discrimination ability. What are the potential or actual clinical implications of this work? The sustained vowel /ɑ/ is a relevant, universal vocal task for clinical application using acoustic measures to discriminate female speakers with and without voice disorders if signal typing is implemented. Clinical voice assessment using vowels may not be effective if relying solely on time-based measurements. Spectral-based measures perform better in voice disorder discrimination given their insensitivity to signal types. The most effective voice disorder discrimination could only be obtained using a combination of acoustic measures that quantify major phenomena in the signals of disordered voices. Using measures extracted from both programs, Praat and ADSV, is useful given that specific settings in a program may impact on discrimination accuracy.

Voice Signal Typing Using a Pattern Recognition Approach.

Voice signal classification in three types according to their degree of periodicity, a task known as signal typing, is a relevant preprocessing step before computing any perturbation measures. However, it is a time consuming and subjective activity. This has given rise to interest in automatic systems that use objective measures to distinguish among the different signal types. The purpose of this paper is twofold. First, to propose a pattern recognition approach for automatic voice signal typing based on a multi-class linear Support Vector Machine, and using rather well-known parameters like Jitter, Shimmer, Harmonic-to-Noise Ratio, and Cepstral Prominence Peak in combination with nonlinear dynamics measures. Two novel features are also proposed as objective parameters. Second, to validate this approach using a large amount of signals coming from two well-known corpora using cross-dataset experiments to assess the generalizability of the system. A total amount of 1262 signals labeled by professional voice pathologists were used with this purpose. Statistically significant differences between all types were found for all features. Accuracies over 82.71% were estimated in all intra-datasets and inter-datasets using cross-validation. Finally, the use of posterior probabilities is proposed as a measure of the reliability of the assigned type. This could help clinicians to make a more informed decision about the type assigned to a voice. These outcomes suggest that the proposed approach can successfully discriminate among the three voice types, paving the way to a fully automatic tool for voice signal typing in the future.

Using Pitch Height and Pitch Strength to Characterize Type 1, 2, and 3 Voice Signals.

OBJECTIVE: Classifying dysphonic voices as type 1, 2, and 3 signals based on their periodicity enables researchers to determine the validity of acoustic measures derived from them. Existing methods of signal typing are commonly performed by listening to the voice sample and visualizing them on narrow-band spectrograms that require training, time, and are subjective in nature. The current study investigated pitch-based metrics (pitch height and pitch strength) as correlates to characterizing voice signal types. The computational estimates were validated with perceptual judgments of pitch height and pitch strength. METHODS: Pitch height and pitch strength were estimated from Auditory-Sawtooth Waveform Inspired Pitch Estimator Prime algorithm for 30 dysphonic voice segments (10 per type). Ten listeners evaluated pitch height through a single-variable matching task and pitch strength through an anchored magnitude estimation task. One way analyses of variance were used to determine the effects of signal type on pitch height and pitch strength estimates. Relationship between computational and perceptual estimates was evaluated using correlation coefficients and their significance. RESULTS: There was a significant difference between signal types in both computational and perceptual pitch strength estimates. Periodic type 1 signals had greater pitch strength compared to type 2 and 3 signals. Auditory-Sawtooth Waveform Inspired Pitch Estimator Prime produced robust computational estimates of pitch height even in type 3 signals when compared to other acoustic software. Listeners were able to reliably judge pitch height in type 2 and 3 signals despite their lack of a clear fundamental frequency. CONCLUSIONS: Pitch height and pitch strength can be measured in all dysphonic voices irrespective of signal periodicity.

Pitch Strength as an Outcome Measure for Treatment of Dysphonia.

BACKGROUND: Measurement of treatment outcomes is critical for the spectrum of voice treatments (ie, surgical, behavioral, or pharmacological). Outcome measures typically include visual (eg, stroboscopic data), auditory (eg, Consensus Auditory-Perceptual Evaluation of Voice; Grade, Roughness, Breathiness, Asthenia, Strain), and objective correlates of vocal fold vibratory characteristics, such as acoustic signals (eg, harmonics-to-noise ratio, cepstral peak prominence) or patient self-reported questionnaires (eg, Voice Handicap Index, Voice-Related Quality of Life). Subjective measures often show high variability, whereas most acoustic measures of voice are only valid for signals where some degree of periodicity can be assumed. However, this assumption is often invalid for dysphonic voices where signal periodicity is suspect. Furthermore, many of these measures are not useful in isolation for diagnostic purposes. OBJECTIVE: We evaluated a recently developed algorithm (Auditory Sawtooth Waveform Inspired Pitch Estimator-Prime [Auditory-SWIPE']) for estimating pitch and pitch strength for dysphonic voices. Whereas fundamental frequency is a physical attribute of a signal, pitch is its psychophysical correlate. As such, the perception of pitch can extend to most signals irrespective of their periodicity. METHODS: Post hoc analyses were conducted for three groups of patients evaluated and treated for voice problems at a major voice center: (1) muscle tension dysphonia/functional dysphonia, (2) vocal fold mass(es), and (3) presbyphonia. All patients were recorded before and after surgical/behavioral treatment for voice disorders. Pitch and pitch strength for each speaker were computed with the Auditory-SWIPE' algorithm. RESULTS: Comparison of pre- and posttreatment data provides support for pitch strength as a measure of treatment outcomes for dysphonic voices.

The Relationship Between Acoustic Signal Typing and Perceptual Evaluation of Tracheoesophageal Voice Quality for Sustained Vowels.

OBJECTIVES: To investigate the relationship between acoustic signal typing and perceptual evaluation of sustained vowels produced by tracheoesophageal (TE) speakers and the use of signal typing in the clinical setting. METHODS: Two evaluators independently categorized 1.75-second segments of narrow-band spectrograms according to acoustic signal typing and independently evaluated the recording of the same segments on a visual analog scale according to overall perceptual acoustic voice quality. The relationship between acoustic signal typing and overall voice quality (as a continuous scale and as a four-point ordinal scale) was investigated and the proportion of inter-rater agreement as well as the reliability between the two measures is reported. RESULTS: The agreement between signal type (I-IV) and ordinal voice quality (four-point scale) was low but significant, and there was a significant linear relationship between the variables. Signal type correctly predicted less than half of the voice quality data. There was a significant main effect of signal type on continuous voice quality scores with significant differences in median quality scores between signal types I-IV, I-III, and I-II. CONCLUSIONS: Signal typing can be used as an adjunct to perceptual and acoustic evaluation of the same stimuli for TE speech as part of a multidimensional evaluation protocol. Signal typing in its current form provides limited predictive information on voice quality, and there is significant overlap between signal types II and III and perceptual categories. Future work should consider whether the current four signal types could be refined.