Background: Anhedonia is a core, clinically consequential feature of depression, and its motivational and behavioral features may be difficult to observe through self-report alone. Speech is an effortful, dynamic, and socially contingent behavior that may index the motivational disruptions central to anhedonia, but few studies have examined anhedonia specifically using a broad, multimodal set of voice features. Objective: We conducted an exploratory, hypothesis-generating analysis of whether voice-derived speech features from a semistructured clinical interview are associated with concurrent anhedonia severity and with anhedonia 4 weeks later, adjusted for baseline severity. Methods: In a secondary analysis of a depression intervention trial, we analyzed recorded 17-item Hamilton Rating Scale for Depression interviews from adults with depression (anhedonia: n=44; depression severity: n=45) collected before participants initiated a behavioral intervention. Of these, 36 provided symptom data at week 4. Acoustic (pitch, spectral or mel-frequency cepstral coefficients, and jitter or shimmer), prosodic, linguistic (lexical richness and sentiment), topic-model, and automatic speech recognition quality features were extracted. After reducing the high-dimensional acoustic set to 9 theory-driven domain composites, 77 features were tested per outcome. Anhedonia (Mood and Anxiety Symptom Questionnaire, Anhedonic Depression Scale) and general depression severity (Quick Inventory of Depressive Symptomatology, Self-Report [QIDS-SR]) were examined with univariate Pearson correlations, with significance assessed by permutation tests and interpreted against a conservative P=.01 threshold, given the exploratory, multiple-testing context. For the week-4 analyses, baseline voice features were correlated, with week-4 symptom scores residualized for baseline symptoms and treatment condition. We report unadjusted P values and emphasize effect sizes, given the exploratory aims. Results: Higher baseline anhedonia was associated with briefer, less redundant speech: higher hapax ratio and lexical diversity (r=0.46; P=.003) alongside fewer content and total words (rs≈–0.43; P≤.01) and shorter total speaking duration (r=–0.41; P=.007), all exceeding the P=.01 threshold. Weaker, subthreshold trends included more negative sentiment (compound r=–0.34; P=.02), less work or school topic content (r=–0.31; P=.04), and flatter pitch variation (fundamental frequency SD r=–0.29; P=.06). General depression severity (QIDS-SR) showed no associations that exceeded the threshold. In baseline-adjusted week-4 analyses, no feature was associated with anhedonia at P=.01, whereas lower baseline voice perturbation (jitter composite; r=–0.46; P=.005) was associated with higher-than-predicted week-4 depression severity. Conclusions: Higher anhedonia aligned most robustly with a voice signature of reduced speech output and sparser but proportionally more diverse speech, with weaker, subthreshold trends toward flatter prosody and more negative sentiment, consistent with diminished behavioral engagement rather than uniform flattening of expressivity. Because the sample was small, predominantly female, and drawn from a depressed sample, and because many features were tested, findings are preliminary and require replication with larger samples and more naturalistic speech.

최근 AI 기반 음성 분석 도구가 고객 응대와 영업 관리에 필수적인 요소로 자리 잡았습니다. 그중에서도 Koala는 간편한 인터페이스로 꽤 인기 있는 서비스입니다. 하지만 실무에 적용하다 보니 분석 정확도나 가격 정책, 연동 기능에서 아쉬움을 느끼는 분들도 많더라고요. 그래서

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Emre Yücelen Şan Dersi 2024Emre Yücelen Vocal Coach Istanbul#SesAnalizi #VoiceAnalysis #VocalAnalysisPR VE İŞBİRLİĞİ ÇALIŞMALARI : sehnazgul@gmail.com (05342...

Tbilisi, Georgia--(Newsfile Corp. - September 8, 2025) - Tbilisi's Business and Technology University (BTU) has created an artificial intelligence system that analyzes voice patterns to identify psychological traits. The technology merges psychology, AI, and psycho-acoustic analytics to reveal the cognitive, emotional, and behavioral dimensions of individuals with notable precision.Business and Technology University (BTU)To view an enhanced version of this graphic, please...

Background: Identifying subtle changes in the menstrual cycle is crucial for effective fertility tracking and understanding reproductive health. Objective: To explore how fundamental frequency features vary between menstrual phases using daily voice…

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A quick voice check could one day become as routine as taking your temperature, helping doctors detect voice box cancer.

The pitch and hoarseness of a person's voice often changes if they have Parkinson's disease, suggesting there could be a non-invasive way of screening for the condition