Mapping acoustic characteristics of emotional prosody in Mandarin disyllabic words: A machine-learning approach

Bright Voice Quality and Fundamental Frequency Variation in Non-binary Speakers

The Relationship Between Pitch Discrimination and Fundamental Frequency Variation: Effects of Singing Status and Vocal Hyperfunction

Emotion-Discrimination Deficits in Mild Alzheimer Disease

Quality of Voice in Patients With Partial Deafness Before and After Cochlear Implantation

Generating tonal distinctions in Mandarin Chinese using an electrolarynx with preprogrammed tone patterns

Introduction. Voice disorders occur in approximately 30% of the country’s population. The most studied characteristics of the voice include fundamental frequency, pitch and amplitude, harmonic-to-noise ratio, cepstral peak severity, acoustic quality index of voice, maximum phonation time, variations in fundamental frequency and number of pauses in speech signals.Aim. Literature review assessing the possibility of acoustic voice analysis in patients with dysphonia.Materials and methods. The authors searched for publications in the electronic databases PubMed, Web of Science, Google Scholar and ELibrary. The search was carried out using the following keywords: “voice acoustic analysis”, “voice disorder”, “artificial neural network”, “dysphonia”, “standard deviation of fundamental frequency”, “voice quality”, “acoustic voice analysis”.Results and discussion. Fundamental frequency may be more sensitive to objective clinical assessment of voice than pitch and amplitude. The severity of the cepstral peak is an integral part of the acoustic analysis of the voice, helping to determine the differences between dysphonic and normal voices. Cepstral analysis is more sensitive to subtle dysphonic changes than vowel analysis methods. Despite the high analytical accuracy, ease of use of machine learning, as well as the promise of this approach in the diagnosis of dysphonia, the clinical application of this technology requires further researchConclusions. Acoustic Analysis of Voice offers numerous advantages such as non-invasiveness, cost-effectiveness, and ease of use, facilitating the acquisition of objective data for evaluating the severity of voice disorders and serving as an indispensable tool for identifying pathologies associated with phonation disturbances. According to the literature, the most informative Acoustic Analysis of Voice parameters include fundamental frequency metrics, pitch and amplitude indices, cepstral peak prominence, voice quality index, maximum phonation time, and the relative noise level in the speech signal.

This paper presents an analysis of speech pauses occurring in selected political speeches, with a focus on both filled and silent types. The paper aims to highlight differences among pause categories in spoken language and assess potential gender differences. By analyzing speeches from male and female speakers in specific syntactic contexts, the paper reveals limited variations in fundamental frequency and pause duration. While certain subcategories exhibit slight differences in frequency, consistent patterns are lacking. Findings indicate that filled, hesitation, politeness, and perturbation pauses tend to be longer, whereas specification and personal stance pauses tend to have lower frequencies. Enumeration, opposition, and segmenting pauses strategically support a speaker's point, aligned with sentence structure. Investigating fundamental frequency trends before pauses demonstrates that a decrease in frequency signifies unit culmination, while an increase suggests non-final positions within units. The paper concludes that disparities exist among pause types, though differences between male and female speakers are generally minor. This emphasizes the need to consider multiple factors, including duration, frequency, and syntactic context, for comprehensive pause definitions. Overall, the paper provides insight into speech pause attributes, variations, and their significance in conveying meaning, thus enriching our understanding of speech patterns and communication strategies.

Background: The Speech-evoked Frequency Following Response (sFFR) provides spctro-temporal data on speech processing in the auditory system. Its effectiveness in extracting prosodic features like variations in fundamental frequency (F0 contour) and intensity is uncertain. Objectives: This study examines how well sFFR tracks F0 contour in different emotions using a natural two-syllable word. It also explores talker’s gender impact on F0 contours and gender disparity in encoding prosodic cues. Method: The word “balloon” spoken by male and female speakers with sad and happy emotions, elicited FFR from 16 adults (8 males, aged 18–31). A pitch estimation algorithm calculated root mean squared error and 5% accuracy to evaluate the response’s fidelity to F0 contour under different conditions. Results: The sFFR tracked prosodic speech features, influenced by emotion type and talker voice characteristics. Participants identified emotions most accurately from sad male voices. Lower F0 trajectories corresponded to more reliable FFR responses, showing better tracking of male voices and sad emotions. No significant gender-related differences were observed in emotional data processing. Conclusion: These findings highlight sFFR’s utility in capturing dynamic speech properties and its potential in clinical assessments. Future research should explore prosody processing in hearing-impaired individuals and consider integrating sFFR into diagnostic protocols.

Multidimensional Scaling of Breathy Voice Quality: Individual Differences in Perception

Recent studies that used adult faces as the baseline have revealed that attentional bias toward infant faces is the strongest for neutral expressions than for happy and sad expressions. However, the time course of the strongest attentional bias toward infant neutral expressions is unclear. To clarify this time course, we combined a behavioral dot-probe task with electrophysiological event-related potentials (ERPs) to measure adults' responses to infant and adult faces with happy, neutral, and sad expressions derived from the same face. The results indicated that compared with the corresponding expressions in adult faces, attentional bias toward infant faces with various expressions resulted in different patterns during rapid and prolonged attention stages. In particular, first, neutral expressions in infant faces elicited greater behavioral attentional bias and P1 responses than happy and sad ones did. Second, sad expressions in infant faces elicited greater N170 responses than neutral and happy ones did; notably, sad expressions elicited greater N170 responses in the left hemisphere in women than in men. Third, late positive potential (LPP) responses were greater for infant faces than for adult faces under each expression condition. Thus, we propose a three-stage model of attentional allocation patterns that reveals the time course of attentional bias toward infant faces with various expressions. This model highlights the prominent role of neutral facial expressions in the attentional bias toward infant faces.

Emotion perception biases may precipitate problematic interpersonal interactions in families affected with borderline personality disorder (BPD) and lead to conflictual relationships. In the present study, the authors investigated the familial aggregation of facial emotion recognition biases for neutral, happy, sad, fearful, and angry expressions in probands with BPD (n = 89), first-degree biological relatives (n = 67), and healthy controls (n = 87). Relatives showed comparable accuracy and response times to controls in recognizing negative emotions in aggregate and most discrete emotions. For sad expressions, both probands and relatives displayed slower response latencies, and they were more likely than controls to perceive sad expressions as fearful. Nonpsychiatrically affected relatives were slower than controls in responding to negative emotional expressions in aggregate, and fearful and sad facial expressions more specifically. These findings uncover potential biases in perceiving sad and fearful facial expressions that may be transmitted in families affected with BPD.

In this study, we investigated the prosody of domestic cat meows produced in different contexts. Prosodic cues – i.e., variation in intonation, duration, voice quality and fundamental frequency – in humans as well as in nonhuman animals carry information about idiosyncratic traits of the signaller, including sex, age, and physical and mental state. The duration, fundamental frequency (F0) and intonation in a sample of 969 meows recorded in seven different contexts (i.e., cuddle, door, food, greeting, lifting, play, cat carrier) were analysed using linear mixed effects regression and generalized additive models. In this, we controlled for cat age and sex, as meows produced by old cats had lower mean F0 than those produced by young cats, and female cats produced meows with higher mean F0 than male cats. We found significant effects of context on duration and mean F0, but not on F0 range. Furthermore, the results showed that the intonation of meows produced by cats in a cat carrier displayed a falling pattern, while that of meows produced in cuddle and door contexts was relatively level, and that of meows produced in the other contexts consisted of combinations of rising and falling. The average slope of meows produced in cat carrier and play contexts was negative, while that of meows produced in the other contexts was positive. We argue that this prosodic variation reflects the cats’ mental or emotional state, because of valence and arousal differences associated with the various contexts that were included in the study. Further studies will need to confirm this. In addition, we also plan additional analyses of spectral and voice quality parameters in meows and other cat vocalisation types.

An implicit assumption in voice perturbation analysis is that one is dealing with small random variations in fundamental frequency and amplitude. However, many irregularities in voiced speech are not necessarily small or random (e.g., subharmonics, amplitude modulations, frequency modulations, linear trends). In this study, various processing schemes are employed to detect/remove trends and subharmonics from fundamental frequency and amplitude contours. Voice perturbation measures are calculated before and after application of the detection/removal techniques. Separating trends and subharmonics from random perturbations in voice analysis may prove useful in classifying and identifying voice disorders. Results of analysis on a variety of subjects will be presented.

A Quantitative Assessment of Lip Movements in Different Facial Expressions Through 3-Dimensional on 3-Dimensional Superimposition: A Cross-Sectional Study

In recent years, the field of automatic speaker identification has begun to exploit high-level sources of speaker-discriminative information, in addition to traditional models of spectral shape. These sources include pronunciation models, prosodic dynamics, pitch, pause, and duration features, phone streams, and conversational interaction. As part of this broader thrust, we explore a new frame-level vector representation of the instantaneous change in fundamental frequency, known as fundamental frequency variation (FFV). The FFV spectrum consists of 7 continuous coefficients, and can be directly modeled in a standard Gaussian mixture model (GMM) framework. Our experiments indicate that FFV features contain useful information for discriminating among speakers, and that model-space combination of FFV and cepstral features outperforms cepstral features alone. In particular, our results on 16kHz Wall Street Journal data show relative reductions in error rate of 54% and 40% for female and male speakers, respectively.