Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Sheikh, Shakeel A."'
Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology irrelevant uncorrelated information, such as changing phonetic content
Externí odkaz:
http://arxiv.org/abs/2409.17276
Despite the promising performance of state of the art approaches for Parkinsons Disease (PD) detection, these approaches often analyze individual speech segments in isolation, which can lead to suboptimal results. Dysarthric cues that characterize sp
Externí odkaz:
http://arxiv.org/abs/2409.07884
Autor:
Sheikh, Shakeel A., Kodrasi, Ina
Automatic pathological speech detection approaches yield promising results in identifying various pathologies. These approaches are typically designed and evaluated for phonetically-controlled speech scenarios, where speakers are prompted to articula
Externí odkaz:
http://arxiv.org/abs/2406.09968
Autor:
Sheikh, Shakeel Ahmad
Speech production is a complex phenomenon, wherein the brain orchestrates a sequence of processes involving thought processing, motor planning, and the execution of articulatory movements. However, this intricate execution of various processes is sus
Externí odkaz:
http://arxiv.org/abs/2406.02572
The adoption of advanced deep learning architectures in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work introduces the application of speech embeddings extracted from pre-traine
Externí odkaz:
http://arxiv.org/abs/2306.00689
Stuttering is a neuro-developmental speech impairment characterized by uncontrolled utterances (interjections) and core behaviors (blocks, repetitions, and prolongations), and is caused by the failure of speech sensorimotors. Due to its complex natur
Externí odkaz:
http://arxiv.org/abs/2302.11343
In this paper, we present end-to-end and speech embedding based systems trained in a self-supervised fashion to participate in the ACM Multimedia 2022 ComParE Challenge, specifically the stuttering sub-challenge. In particular, we exploit the embeddi
Externí odkaz:
http://arxiv.org/abs/2207.10817
By automatic detection and identification of stuttering, speech pathologists can track the progression of disfluencies of persons who stutter (PWS). In this paper, we investigate the impact of multi-task (MTL) and adversarial learning (ADV) to learn
Externí odkaz:
http://arxiv.org/abs/2204.01735
The adoption of advanced deep learning (DL) architecture in stuttering detection (SD) tasks is challenging due to the limited size of the available datasets. To this end, this work introduces the application of speech embeddings extracted with pre-tr
Externí odkaz:
http://arxiv.org/abs/2204.01564
Stuttering is a speech disorder during which the flow of speech is interrupted by involuntary pauses and repetition of sounds. Stuttering identification is an interesting interdisciplinary domain research problem which involves pathology, psychology,
Externí odkaz:
http://arxiv.org/abs/2107.04057