Vocal Analysis Research Articles

Utilizing DeepSqueak for automatic detection and classification of mammalian vocalizations: a case study on primate vocalizations

Bioacoustic analyses of animal vocalizations are predominantly accomplished through manual scanning, a highly subjective and time-consuming process. Thus, validated automated analyses are needed that are usable for a variety of animal species and easy to handle by non-programing specialists. This study tested and validated whether DeepSqueak, a user-friendly software, developed for rodent ultrasonic vocalizations, can be generalized to automate the detection/segmentation, clustering and classification of high-frequency/ultrasonic vocalizations of a primate species. Our validation procedure showed that the trained detectors for vocalizations of the gray mouse lemur (Microcebus murinus) can deal with different call types, individual variation and different recording quality. Implementing additional filters drastically reduced noise signals (4225 events) and call fragments (637 events), resulting in 91% correct detections (Ntotal = 3040). Additionally, the detectors could be used to detect the vocalizations of an evolutionary closely related species, the Goodman’s mouse lemur (M. lehilahytsara). An integrated supervised classifier classified 93% of the 2683 calls correctly to the respective call type, and the unsupervised clustering model grouped the calls into clusters matching the published human-made categories. This study shows that DeepSqueak can be successfully utilized to detect, cluster and classify high-frequency/ultrasonic vocalizations of other taxa than rodents, and suggests a validation procedure usable to evaluate further bioacoustics software.

Management and reduction of fraud by analysis of the insurer’s internal and external vulnerabilities and contributions

"Fraud is a dynamic phenomenon: when the industry discovers a „scam” – a specific pattern of fraud – and sets up barriers to prevent it from recurring, another scam takes place – a new pattern is developed by fraudsters. The motivations for committing fraud vary: from opportunists who submit a fraudulent request as a way to recover their insurance premium, probably encouraged by a change in public attitudes towards crime without victims to criminal networks that use fraud as a regular source of revenue generation. The legal, organizational and commercial constraints under which the insurance industry operates often have a negative impact on existing fraud prevention, detection and investigation practices. Initially, the fight against insurance fraud focused on identifying the characteristics of fraudulent claims and fraudulent claimants – the empirical phase / traditional approach and the role that insurance company employees played in this phase was a major one. Then, given the wave of interest in the insurance industry, emphasis was placed on the use of approaches such as: a) cognitive interviewing and vocal stress analysis that were used in forensic investigation, as well; b) data extraction techniques, specially developed for fraud detection. Are just these effective? The material will try to analyze the vulnerabilities and contributions, both internal and external, that are introduced by the policies, procedures and controls implemented by the insurer within the organization."

Integrative taxonomy of Flatbill Flycatchers (Tyrannidae) reveals a new species from the Amazonian lowlands

AbstractIntegrative taxonomic studies continue to reveal that many current polytypic species of birds are in fact constituted by two or more species and therefore have been central in uncovering ‘hidden’ or ‘cryptic’ biodiversity. The Olivaceous Flatbill (Aves: Tyrannidae: Rhynchocyclus olivaceus) currently has nine recognized subspecies distributed throughout the Neotropics, but so far, no complete phylogenetic hypothesis exists to test the validity and evolutionary relationships among them. To remedy this, we conducted a multi‐character integrative taxonomic revision of the genus Rhynchocyclus, focusing on the polytypic R. olivaceus. The combination of a taxonomically dense sampled multilocus phylogeny (including three mitochondrial and two nuclear genes) with phenotypic analyses including morphological and vocal characters pointed to several taxonomic inconsistencies within R. olivaceus. The analyses strongly support that R. olivaceus is paraphyletic, with an exclusively cis‐Andean clade (where the topotypic R. olivaceus is found) clustering as sister to Rhynchocyclus fulvipectus, to the exclusion of a clade grouping trans‐Andean and western Amazonian populations currently placed in R. olivaceus—one of which is unnamed and fully diagnosable based on vocal and genetic characters. Consistent with the phylogenetic results, our vocal analyses identified at least four morphologically cryptic lineages within R. olivaceus that can be mutually diagnosed from each other by different loudsongs and call parameters. Therefore, we provide evidence for splitting these four groups into separate species, two of which are sympatric but not syntopic in western Amazonia, including an unnamed species described herein—Rhynchocyclus cryptus, sp. nov. urn:lsid:zoobank.org:act:2DC17190‐2BDD‐49EC‐88E6‐4CF2FC2562A3.

Survey of Long-tailed macaque’s Behaviour in Mount Rinjani National Park, Lombok Timur

The research was conducted in Mount Rinjani National Park, Lombok Timur, to observe the daily activities of Long-tailed macaque in utilizing their habitat and vocalization analysis as a hallmark of species. The Long-tailed macaque’ activities were analyzed using the scan sampling method. Ten (10) individuals of Long-tailed macaque were observed. The results showed that the observed activities of Long-tailed macaque were feeding, sleeping, inactive, grooming, mating, moving, excretion, playing, making sound, and agonistic (fighting). The average percentage of Long-tailed macaque observed was feeding (10.1%), sleeping (9%), inactive (9.4%), grooming (14.9%), mating (8.5%), moving (11.1%), excretion (7.9%), playing (9.7%), making sound (9.5%), and agonistic (9.9%). The daily activities can be as a reference in detecting the ‘species’ of the Long-tailed macaque. To support the population of this species, especially for long-term survival, the manager of the Rinjani Nasional Park should preserve the forest with conservation of natural forests and wildlife or biodiversity conservation and ecotourism, also prevent littering and illegal activities.

A new subspecies in the Turdus nudigenis complex (Aves: Turdidae) from the Cauca River valley of Colombia.

The taxonomy of the T. nudigenis complex has historically been challenging due to the uniformity in plumage among taxa, poorly known vocal variation, and allopatric distributions. Recent phylogenetic analyses have contributed to understanding relationships within the genus; however, much remains to be learned about the extent of phenotypic and genetic differentiation within taxa and its implications in species limits. Here, we analyze the taxonomic status of an enigmatic member of the T. nudigenis complex restricted to the Cauca River valley of Colombia. Our phylogenetic analyses suggest that the Cauca valley population is genetically distinctive from other members of the complex, although its sister relationships with respect to T. grayi and T. nudigenis could not be fully resolved due to paraphyly in the group. Based on similarity of plumage, calls, and biogeographic proximity we tentatively treat this taxon as a new subspecies of T. grayi. Populations from the northern Caribbean T. grayi casius/incomptus and the new taxon inhabit similar environmental conditions, being apparently isolated by the humid Nech lowlands. A formal analysis of vocalizations coupled with a more robust genomic dataset are needed to resolve the systematic affinities and taxonomic status of several members of the T. nudigenis complex.

A taxonomic assessment of Chlorospingus flavopectus phaeocephalus and Chlorospingus semifuscus (Passeriformes: Passerellidae), including the description of a new subspecies.

Chlorospingus flavopectus, a widely distributed member of the New World sparrows and finches (Passerellidae), is among the most variable and complex Neotropical bird species. With up to 28 subspecies that inhabit montane forest from Mexico south to Argentina, it presents a recurring leapfrog pattern, with many genetically differentiated lineages, but even more morphologically distinguishable taxa. Chlorospingus flavopectus phaeocephalus is distributed along the eastern Andean slope (from southern Colombia to northern Peru) and in localized patches along the central and southwestern slopes of the Ecuadorian Andes, in the forest remnants of Chimborazo, El Oro, and adjacent Loja provinces. This allopatric distribution was based on plumage similarities, but no genetic or vocal analysis has tested if these populations share a common ancestry. Here, we compared data for populations of C. f. phaeocephalus, C. semifuscus semifuscus from northwest Ecuador, and C. s. livingstoni from west Colombia, and analyzed them in the context of other Chlorospingus. Our phylogenetic analyses revealed that the putative west Ecuadorian populations of C. f. phaeocephalus are nested within C. semifuscus. They also present diagnostic characters when compared to other populations of C. semifuscus and C. f. phaeocephalus. Based on these results, we recognize the population from El Oro and adjacent Loja Provinces as a new subspecies of C. semifuscus, and the population from Chimborazo Province as a morphological variation of C. s. semifuscus.

Applications of Artificial Intelligence to Office Laryngoscopy: A Scoping Review.

This scoping review aims to provide a broad overview of the applications of artificial intelligence (AI) to office laryngoscopy to identify gaps in knowledge and guide future research. Scoping Review. Searches for studies on AI and office laryngoscopy were conducted in five databases. Title and abstract and then full-text screening were performed. Primary research studies published in English of any date were included. Studies were summarized by: AI applications, targeted conditions, imaging modalities, author affiliations, and dataset characteristics. Studies focused on vocal fold vibration analysis (43%), lesion recognition (24%), and vocal fold movement determination (19%). The most frequently automated tasks were recognition of vocal fold nodules (19%), polyp (14%), paralysis (11%), paresis (8%), and cyst (7%). Imaging modalities included high-speed laryngeal videos (45%), stroboscopy (29%), and narrow band imaging endoscopy (7%). The body of literature was primarily authored by science, technology, engineering, and math (STEM) specialists (76%) with only 30 studies (31%) involving co-authorship by STEM specialists and otolaryngologists. Datasets were mostly from single institution (84%) and most commonly originated from Germany (23%), USA (16%), Spain (9%), Italy (8%), and China (8%). Demographic information was only reported in 39 studies (40%), with age and sex being the most commonly reported, whereas race/ethnicity and gender were not reported in any studies. More interdisciplinary collaboration between STEM and otolaryngology research teams improved demographic reporting especially of race and ethnicity to ensure broad representation, and larger and more geographically diverse datasets will be crucial to future research on AI in office laryngoscopy. NA Laryngoscope, 132:1993-2016, 2022.

Carrots or sticks in debt collection services? A voice metrics and text analysis of debt collection calls

PurposeAlthough phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.Design/methodology/approachThe authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.FindingsThe findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.Originality/valueThe findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.

Numerical analysis and physical consideration of vocal fold vibration by modal analysis

Vocal fold vibration is self-excited vibration due to the interaction between the vocal folds and expired air. To analyze vocal fold vibration, several models comprising masses and springs have been proposed to date, but with those models it is difficult to determine parameter values and calculate the phenomenon in detail. Also, several researchers have analyzed vocal fold vibration using the finite element method (FEM) and the finite volume method, but such large-degree-of-freedom analyses incur high computational cost. To solve these problems, we propose a vocal fold vibration analysis model based on modal analysis. The natural modes of the vocal folds are obtained by FEM, and the air is simulated by a one-dimensional model based on Bernoulli's equation. In this approach, because the natural modes are obtained by FEM, parameters based on actual physical properties (Young's modulus, etc.) can be used, and the calculation cost is relatively low because of the modal and one-dimensional fluid analyses. To validate the proposed model, its results are compared with those from experiments on silicone vocal folds and simulations using ANSYS software, and the results correspond. Furthermore, the necessary conditions for the vocal folds to enter two particular modes of self-excited vibration are derived mathematically, and those two modes are confirmed to be dominant in the periodic solution region.

Vocalisations, taxonomy and nomenclature of the pied boubous of eastern Africa

A vocal analysis of the duets of pied boubous Laniarius spp. across eastern Africa is presented, focusing on birds from coastal Somalia south to Mozambique. Based on the presence or absence of certain note types in duets, and variation in their structure across different populations in this region, forms of duet were found to cluster at both larger and smaller spatial scales. Vocal congruence suggests that taxon mossambicus could be conspecific with sublacteus, whereas marked differences between these two and coastal Kenyan birds confirm their previously reported genetic distinctiveness. Patterns of vocal variation broadly align with taxonomic divisions already indicated by genetic data and I integrate these to identify and define the ranges of four distinct groups: the Tropical (major and ambiguus), Ethiopian (aethiopicus), Somali (somaliensis) and East Coast groups (sublacteus, mossambicus and extralimital limpopoensis). Species rank for birds in coastal Kenya under the name Juba Boubou L. somaliensis is also proposed, and vocal data presented here support the findings of Nguembock et al. (2008) and Finch et al. (2016) that plumage criteria are unreliable indicators of taxonomy in Laniarius.

Evaluating the DeepSqueak and Mouse Song Analyzer vocalization analysis systems in C57BL/6J, FVB.129, and FVB neonates

BackgroundCommunication is an essential behavior in mammals. Alterations in communication (neonatal crying) characterize numerous human neurodevelopmental conditions. Mice produce communicative vocalizations, known as ultrasonic vocalizations, (USVs) that can be recorded. The Mouse Song Analyzer is an automated USV analysis system while DeepSqueak is a semi-automated USV detection system. MethodWe used data from, C57BL/6J, FVB.129, and FVB neonates to compare the reliability of DeepSqueak and the Mouse Song Analyzer across various acoustic variables. ResultsWe found that both systems detected a similar quantity of USVs for FVB.129 and FVB mice. However, DeepSqueak detected more USVs for C57BL/6J mice. High correlations were found between systems for each strain. When assessing duration, Deepsqueak detected USVs of a longer duration then the Mouse Song Analyzer across all strains. A low correlation between systems for duration was found for FVB.129 mice, while high correlations were found for C57BL/6J and FVB mice. When assessing fundamental frequency, the Mouse Song Analyzer detected a higher frequency than DeepSqueak for FVB.129 mice, with no other differences present. High correlations between systems were found for C57BL/6J and FVB.129 mice, while a low correlation was found for FVB mice. We also assessed each system’s sensitivity and found that Deepsqueak was able to detect softer USVs than the Mouse Song Analyzer. ConclusionsThese findings demonstrate that the strain of mouse used significantly affects the reliability of USV analysis systems. However, our data also indicates that DeepSqueak is more reliable and accurate than the Mouse Song Analyzer due to its increased sensitivity.

Vocal Folds Analysis for Detection and Classification of Voice Disorder

The detection and description of pathological voice are the most important applications of voice profiling. Currently, techniques like laryngostroboscopy or surgical microlarynoscopy are popularly used for the diagnosis of voice pathologies but are invasive in nature. Disorders of vocal folds impact the quality of voice, and therefore, the accuracy of voice profiling is reduced. This paper presents a better solution to differentiate normal and pathological voices based on the glottal, physical, and acoustic and equivalent electrical parameters. These parameters have been correlated using mathematical equations and models. Results reveal that the glottal flow is strongly influenced by physical parameters like stiffness and viscosity of vocal folds in case of pathological voice. However, their direct measurement requires complex invasive medical procedures or costly and complex electronic hardware arrangements in case of non-invasive methods. Glottal parameters, on the other hand, facilitate much simpler estimation of vocal folds disorders. In this work, the authors have presented two non-invasive approaches for better accuracy and least complexity for differentiating normal and pathological voices: 1) by using correlation of glottal and physical parameters, 2)by using acoustic and equivalent electrical parameters.

Dopaminergic signaling supports auditory social learning

Explicit rewards are commonly used to reinforce a behavior, a form of learning that engages the dopaminergic neuromodulatory system. In contrast, skill acquisition can display dramatic improvements from a social learning experience, even though the observer receives no explicit reward. Here, we test whether a dopaminergic signal contributes to social learning in naïve gerbils that are exposed to, and learn from, a skilled demonstrator performing an auditory discrimination task. Following five exposure sessions, naïve observer gerbils were allowed to practice the auditory task and their performance was assessed across days. We first tested the effect of an explicit food reward in the observer’s compartment that was yoked to the demonstrator’s performance during exposure sessions. Naïve observer gerbils with the yoked reward learned the discrimination task significantly faster, as compared to unrewarded observers. The effect of this explicit reward was abolished by administration of a D1/D5 dopamine receptor antagonist during the exposure sessions. Similarly, the D1/D5 antagonist reduced the rate of learning in unrewarded observers. To test whether a dopaminergic signal was sufficient to enhance social learning, we administered a D1/D5 receptor agonist during the exposure sessions in which no reward was present and found that the rate of learning occurred significantly faster. Finally, a quantitative analysis of vocalizations during the exposure sessions suggests one behavioral strategy that contributes to social learning. Together, these results are consistent with a dopamine-dependent reward signal during social learning.

High Predictive Accuracy of Negative Schizotypy With Acoustic Measures.

Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.

Acoustic Measures of Brazilian Transgender Women's Voices: A Case-Control Study.

Objective: This study aims to compare the acoustic vocal analysis results of a group of transgender women relative to those of cisgender women.Methods: Thirty transgender women between the ages of 19 and 52 years old participated in the study. The control group was composed of 31 cisgender women between the ages of 20 and 48 years old. A standardized questionnaire was administered to collect general patient data to better characterize the participants. The vowel /a/ sounds of all participants were collected and analyzed by the Multi-Dimensional Voice Program advanced system.Results: Statistically significant differences between cisgender and transgender women were found on 14 measures: fundamental frequency, maximum fundamental frequency, minimum fundamental frequency, standard deviation of fundamental frequency, absolute jitter, percentage or relative jitter, fundamental frequency relative average perturbation, fundamental frequency perturbation quotient, smoothed fundamental frequency perturbation quotient, fundamental frequency variation, absolute shimmer, relative shimmer, voice turbulence index (lower values in the cases), and soft phonation index (higher values in the cases). The mean fundamental frequency value was 159.046 Hz for the cases and 192.435 Hz for the controls.Conclusion: Through glottal adaptations, the group of transgender women managed to feminize their voices, presenting voices that were less aperiodic and softer than those of cisgender women.

Separating overlapping bat calls with a bi-directional long short-term memory network.

Acquiring clear acoustic signals is critical for the analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, which can impede the structural identification of vocal units, but there is currently no satisfactory solution. This study presents a bi-directional long short-term memory network to separate overlapping echolocation-communication calls of 6 different bat species and reconstruct waveforms. The separation quality was evaluated using 7 temporal-spectrum parameters. All the echolocation pulses and syllables of communication calls in the overlapping signals were separated and parameter comparisons showed no significant difference and negligible deviation between the extracted and original calls. Clustering analysis was conducted with separated echolocation calls from each bat species to provide an example of practical application of the separated and reconstructed calls. The result of clustering analysis showed high corrected rand index (82.79%), suggesting the reconstructed waveforms could be reliably used for species classification. These results demonstrate a convenient and automated approach for separating overlapping calls. The study extends the application of deep neural networks to separate overlapping animal sounds.

Morphology, vocalizations, and mitochondrial DNA suggest that the Graceful Prinia is two species

Abstract Prinias (Cisticolidae: Prinia) are resident warblers of open areas across Africa and Asia and include many polytypic species whose species limits have not been seriously reevaluated recently. Based on an integrative taxonomic analysis of morphology, song, and mitochondrial DNA (mtDNA), we suggest that 2 species should be recognized in the Graceful Prinia (Prinia gracilis) complex. In addition, our morphological analyses show the existence of a well-marked undescribed form in southeastern Somalia, which we name herein as a new subspecies. Prinia gracilis is a small, drab, long-tailed species with streaking above and plain pale underparts that has been suggested to fall into 2 groups: the southwestern nominate group (from Egypt to Oman) and the northeastern lepida group (from Turkey through India). However, the characters presented to justify this grouping are variable and show a mosaic pattern, and whether genetic and vocal differences exist is unknown. We found consistent between-group song differences, with the nominate group giving consistently longer inter-phrase intervals, whereas the members of the lepida group sing an essentially continuous reel. An mtDNA tree suggests a deep split between the nominate and lepida groups, with a coalescence time between these clades of ~ 2.2 million years ago. Vocal and mtDNA analyses provided evidence that the northeastern Arabian Peninsula taxon carpenteri belongs to the lepida group. We found that, of all the morphological characters proposed, only proportions and tail barring and spotting relatively consistently distinguish the 2 groups. However, these characters strongly suggest that the eastern Arabian Peninsula is populated by taxa of both the gracilis and lepida groups, in different areas, but we lack genetic and bioacoustic data to corroborate this. Although further study is needed in potential contact zones, we suggest that 2 species should be recognized in the P. gracilis complex, and we propose the retention of the English name Graceful Prinia for P. gracilis sensu stricto, while we suggest that P. lepida be known as Delicate Prinia.

Accuracy of Laryngoscopy for Quantitative Vocal Fold Analysis in Combination with AI, A Cohort Study of Manual Artefacts

Flexible endoscopy was used in examination cervix lesion the first was studied by Nishiyama et al was reported the used of endoscopy for diagnosing cervical neoplasms. The second study of K Uchita et al.

Automated ultrasonic vocalization analysis: Training and testing VocalMat on a rat-based dataset

Background: Ultrasonic vocalizations (USVs) offer another way to study the behaviour of rodents in addition to commonly used visual methods. USV subtypes have been associated with behaviour such as the concurrence of 22-kHz calls and signs of distress (defensive behaviour). (1,2) However, the categories used to analyze USVs are a source of contention, most notably with 50-kHz calls, and may even be arbitrary altogether. (3) To facilitate subtyping calls, VocalMat has been developed for USV identification and classification, and it has shown an accuracy of greater than 98% for mice USV detection and 86% for mice USV classification. (4) In this project, we have constructed a rat-based dataset of USVs and then used it to train the VocalMat program to assess automated USV classification. Methods: Avisoft-SASLab Pro was used to manually classify USVs from 216 audio files. The sorted USVs were then used to train VocalMat’s classification program. Results: Our results show overall accuracies greater than 90% with the highest in the trill and flat categories (97.2% and 91.0%). We experimented with the number of USV categories and found high accuracies when grouping spectrographically similar calls, which are flat calls with up and down ramp calls (96.9%) and trill calls with trill jump and flat-trill calls (98.7%). Limitations: There are large variations in the number of calls per category in our dataset. More data is needed to fill these gaps and provide more training samples for infrequent calls. Conclusions: By creating a database of rat USVs and then using it to train VocalMat, we have shown the potential of its adaption to a rat vocal repertoire. Going forward, we hope to test more variations of USV categories on machine learning programs to establish a robust approach to classifying USVs.

Patient Self-Assessment and Acoustic Voice Analysis in Screening of Postoperative Vocal Fold Paresis and Paralysis.

Background and objective:The aim of this study was to evaluate the utility of two items in vocal fold paresis and paralysis screening after thyroid and parathyroid surgery: patient self-assessment of voice using the Voice Handicap Index and computer-based acoustic voice analysis using the Multi-Dimensional Voice Program.Methods:This was a prospective study of 181 patients who underwent thyroid or parathyroid surgery over a 1-year study period (2017). Preoperatively, all patients underwent laryngoscopic vocal fold inspection and acoustic voice analysis, and they completed the Voice Handicap Index questionnaire. Postoperatively, all patients underwent laryngoscopy prior to hospital discharge; 2 weeks after the surgery, they completed the Voice Handicap Index questionnaire a second time. Two weeks postoperatively, patients with vocal fold paresis or paralysis and 20 randomly selected controls without vocal fold paresis or paralysis underwent a follow-up acoustic voice analysis.Results:Fourteen patients had a new postoperative vocal fold paresis or paralysis. Postoperatively, the total Voice Handicap Index score was significantly higher (p = 0.040) and the change between preoperative and postoperative scores was greater (p = 0.028) in vocal fold paresis or paralysis patients. A total postoperative Voice Handicap Index score > 30 had 55% sensitivity, and 90% specificity, for vocal fold paresis or paralysis. In the postoperative Multi-Dimensional Voice Program analysis, vocal fold paresis or paralysis patients had significantly more jitter (p = 0.044). Postoperative jitter > 1.33 corresponded to 55% sensitivity, and 95% specificity, for vocal fold paresis or paralysis.Conclusions:In identifying postoperative vocal fold paresis or paralysis, patient self-assessment and jitter in acoustic voice analysis have high specificity but poor sensitivity. Without routine laryngoscopy, approximately half of the patients with postoperative vocal fold paresis or paralysis could be overlooked. However, if the patient has no complaints of voice disturbance 2 weeks after thyroid or parathyroid surgery, the likelihood of vocal fold paresis or paralysis is low.