In the clinical practice of dysphonia, the effects of treatment are traditionally monitored by a sequence of auditory-perceptual assessments aimed at measuring vocal quality for the patient. Alternatively, acoustic measurement of vocal quality promises to automate perceptual assessments while keeping the assessments accurate and non-invasive. However, acoustic measures of vocal quality need to be further developed in both functional and technical terms. On the one hand, many of them are susceptible to non-dysphonic perturbations from articulatory movements in continuous speech, while on the other, their accuracy in approximating the generally nonlinear mapping from observation to vocal quality is limited by their use of a linear model. This paper presents an acoustic measure of vocal strain, a specific vocal quality that typically co-occurs with the development of vocal-fold nodules in vocal hyper-function. Vocal strain merits acoustic measurement more than other vocal qualities because its perceptual assessment typically exhibits a lower intra- and inter-rater reliability than the assessment of other vocal qualities. Based on an assumed correlation between vocal strain and the degree of periodicity in vocal-fold vibrations, this paper presents an acoustic measure in which a nonlinear regression model is used to predict the strain from some periodicity features extracted from a glottal airflow estimate. When tested on a set of listener-rated utterances composed mostly of continuous speech, the proposed glottal measure outperformed a direct-analysis measure in producing strain assessments which are consistent with perceptual ratings.

Glottal inverse filtering aims to estimate the glottal airflow signal from a speech signal for applications such as speaker recognition and clinical voice assessment. Nonetheless, evaluation of inverse filtering algorithms has been challenging due to the practical difficulties of directly measuring glottal airflow. Apart from this, it is acknowledged that the performance of many methods degrade in voice conditions that are of great interest, such as breathiness, high pitch, soft voice, and running speech. This paper presents a comprehensive, objective, and comparative evaluation of state-of-the-art inverse filtering algorithms that takes advantage of speech and glottal airflow signals generated by a physiological speech synthesizer. The synthesizer provides a physics-based simulation of the voice production process and thus an adequate test bed for revealing the temporal and spectral performance characteristics of each algorithm. Included in the synthetic data are continuous speech utterances and sustained vowels, which are produced with multiple voice qualities (pressed, slightly pressed, modal, slightly breathy, and breathy), fundamental frequencies, and subglottal pressures to simulate the natural variations in real speech. In evaluating the accuracy of a glottal flow estimate, multiple error measures are used, including an error in the estimated signal that measures overall waveform deviation, as well as an error in each of several clinically relevant features extracted from the glottal flow estimate. Waveform errors calculated from glottal flow estimation experiments exhibited mean values around 30% for sustained vowels, and around 40% for continuous speech, of the amplitude of true glottal flow derivative. Closed-phase approaches showed remarkable stability across different voice qualities and subglottal pressures. The algorithms of choice, as suggested by significance tests, are closed-phase covariance analysis for the analysis of sustained vowels, and sparse linear prediction for the analysis of continuous speech. Results of data subset analysis suggest that analysis of close rounded vowels is an additional challenge in glottal flow estimation.

This study addresses the task of aligning lyrics with accompanied singing recordings. With a vowel-only representation of lyric syllables, our approach evaluates likelihood scores of vowel types with glottal pulse shapes and formant frequencies extracted from a small set of singing examples. The proposed vowel likelihood model is used in conjunction with a prior model of frame-wise syllable sequence in determining an optimal evolution of syllabic position. In lyrics alignment experiments, we optimized numerical parameters on two independent development sets and then tested the optimized system on two other datasets. New objective performance measures are introduced in the evaluation to provide further insight into the quality of alignment. Use of glottal pulse shapes and formant frequencies is shown by a controlled experiment to account for a 0.07 difference in average normalized alignment error. Another controlled experiment demonstrates that, with a difference of 0.03, F0-invariant glottal pulse shape gives a lower average normalized alignment error than does F0-invariant spectrum envelope, the latter being assumed by MFCC-based timbre models.

This work considers the task of estimating the source and filter from human voice signals. Since the energy of voiced sound concentrates on discrete frequencies, a notable challenge with this task would be that higher pitches in the signal can make the harmonically related frequency response samples of the vocal tract filter an incomplete representation. In view of this, we propose to model the magnitude and phase response of the first formant as an alternative to the minimum phase property of the vocal tract filter. In particular, the magnitude response of the vocal tract filter sampled at the first three partials only, is sufficient for determining the phase response of the first formant. We verified our new method with glottal pulse shape parameter estimation experiments conducted on the CMU Arctic dataset, which showed that single-formant filter is an adequate alternative to minimum-phase filter in vocal tract modeling for glottal pulse shape estimation.

This paper presents a novel approach to extraction of vocal melodies from accompanied singing recordings. Central to our approach is a model of vocal fundamental frequency (F0) likelihood that integrates acoustic-phonetic knowledge and real-world data. This model consists of a timbral fitness score and a loudness measure of each F0 candidate. Timbral fitness is measured for the partial amplitudes of an F0 candidate, with respect to a small set of vocal timbre examples. This F0-specific measurement of timbral fitness depends on an acoustic-phonetic F0 modification of each timbre example. In the loudness part of the likelihood model, sinusoids are detected, tracked, and pruned to give loudness values that minimize interference from the accompaniment. A final F0 estimate is determined by a prior model of F0 sequence in addition to the likelihood model. Melody extraction is completed by detecting voiced time positions according to the singing voice loudness variations given by the estimated F0 sequence. The numerical parameters involved in our approach were optimized on three development sets from different sources before the system was evaluated on ten test sets separate from these development sets. Controlled experiments show that use of the timbral fitness score accounts for a 13% difference in overall accuracy.

Octave detection has been an open issue in automatic transcription of polyphonic music over the years. In the literature, pitch detectors for polyphonic music usually fail to detect octaves for lack of information about the timbre of each instrument that appears in the music. To reveal the possibility that this difficulty in automatic music transcription can be overcome, here a music transcription system that is capable of octave detection is proposed. Preceded by a constant-Q time-frequency analysis front end implemented with a fast algorithm for nonorthonormal discrete wavelet transform [1], a classifier using support vector machine technique [2] serves as a novel octave detector in this system.

A tight estimate of upper and lower bounds of the distance between convex polyhedra based on the best ellipsoid fit is proposed. Estimated distance is mainly based on enclosed ellipsoids, instead of minimum volume enclosing ellipsoids. We provide an algorithm for computing the enclosed ellipsoid of a convex polyhedron by the use of its best fit enclosing ellipsoid. By this estimate, the collision-free region could be much larger than enclosing ellipsoids and the detection of potential collisions can be more accurate than that of using enclosing ellipsoids. A numerical example is presented to show the tightness of upper and lower distance estimates based on enclosed ellipsoids.