scholarly journals Robust Unsupervised Speaker Segmentation for Audio Diarization

10.5772/8540 ◽  
2010 ◽  
Author(s):  
Hachem Kadri ◽  
Manuel Davy ◽  
Noureddine Ellouze
Keyword(s):  
Speaker Segmentation

Evolutive speaker segmentation using a repository system

2004 ◽  
Author(s):  
Xavier Anguera Miro ◽  
Javier Hernando Pericas
Keyword(s):  
Speaker Segmentation

scholarly journals Fully automated cognitive screening tool based on assessment of speech and language

2020 ◽  
Vol 92 (1) ◽  
pp. 12-15
Author(s):  
Ronan Peter Daniel O'Malley ◽  
Bahman Mirheidari ◽  
Kirsty Harkness ◽  
Markus Reuber ◽  
Annalena Venneri ◽  
...  
Keyword(s):  
Cognitive Disorders ◽  
Cognitive Disorder ◽  
Current Data ◽  
Automated System ◽  
Cognitive Screening ◽  
Machine Learning Classification ◽  
Speaker Segmentation ◽  
Memory Clinics ◽  
Memory Concerns ◽  
Fully Automated System

IntroductionRecent years have seen an almost sevenfold rise in referrals to specialist memory clinics. This has been associated with an increased proportion of patients referred with functional cognitive disorder (FCD), that is, non-progressive cognitive complaints. These patients are likely to benefit from a range of interventions (eg, psychotherapy) distinct from the requirements of patients with neurodegenerative cognitive disorders. We have developed a fully automated system, ‘CognoSpeak’, which enables risk stratification at the primary–secondary care interface and ongoing monitoring of patients with memory concerns.MethodsWe recruited 15 participants to each of four groups: Alzheimer’s disease (AD), mild cognitive impairment (MCI), FCD and healthy controls. Participants responded to 12 questions posed by a computer-presented talking head. Automatic analysis of the audio and speech data involved speaker segmentation, automatic speech recognition and machine learning classification.ResultsCognoSpeak could distinguish between participants in the AD or MCI groups and those in the FCD or healthy control groups with a sensitivity of 86.7%. Patients with MCI were identified with a sensitivity of 80%.DiscussionOur fully automated system achieved levels of accuracy comparable to currently available, manually administered assessments. Greater accuracy should be achievable through further system training with a greater number of users, the inclusion of verbal fluency tasks and repeat assessments. The current data supports CognoSpeak’s promise as a screening and monitoring tool for patients with MCI. Pending confirmation of these findings, it may allow clinicians to offer patients at low risk of dementia earlier reassurance and relieve pressures on specialist memory services.

A Novel Speaker Segmentation Method Fused with Acoustic Event Information

2016 ◽  
Author(s):  
Ze-ping XIAO ◽  
Gang TANG ◽  
Tong LI ◽  
Xiang XIE ◽  
Shi-lei HUANG ◽  
...  
Keyword(s):  
Segmentation Method ◽  
Speaker Segmentation ◽  
Acoustic Event ◽  
Event Information
Sign in / Sign up

Export Citation Format

Share Document