Disentangled Latent Speech Representation for Automatic Pathological Intelligibility Assessment
Speech intelligibility assessment plays an important role in the therapy of patients suffering from pathological speech disorders. Automatic and objective measures are desirable to assist therapists in their traditionally subjective and labor-intensive assessments. In this work, we investigate a novel approach for obtaining such a measure using the divergence in disentangled latent speech representations of a parallel utterance pair, obtained from a healthy reference and a pathological speaker. Experiments on an English database of Cerebral Palsy patients, using all available utterances per speaker, show high and significant correlation values (R = -0.9) with subjective intelligibility measures, while having only minimal deviation (+-0.01) across four different reference speaker pairs. We also demonstrate the robustness of the proposed method (R = -0.89 deviating +-0.02 over 1000 iterations) by considering a significantly smaller amount of utterances per speaker. Our results are among the first to show that disentangled speech representations can be used for automatic pathological speech intelligibility assessment, resulting in a reference speaker pair invariant method, applicable in scenarios with only few utterances available.
Code (1)
Similar Papers 제목 키워드 기반
Unsupervised Learning of Disentangled and Interpretable Representations from Sequential Data
We present a factorized hierarchical variational autoencoder, which learns disentangled and interpretable representations from sequential data without supervision. Specifically, we exploit the multi-scale nature of infor…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Speaker Verificationspeech-recognition+1Multiview Canonical Correlation Analysis for Automatic Pathological Speech Detection
Recently proposed automatic pathological speech detection approaches rely on spectrogram input representations or wav2vec2 embeddings. These representations may contain pathology irrelevant uncorrelated information, such…
Dimensionality ReductionUnsupervised Speech Domain Adaptation Based on Disentangled Representation Learning for Robust Speech Recognition
In general, the performance of automatic speech recognition (ASR) systems is significantly degraded due to the mismatch between training and test environments. Recently, a deep-learning-based image-to-image translation t…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Domain AdaptationImage-to-Image Translation+5Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation
End-to-end transformer-based automatic speech recognition (ASR) systems often capture multiple speech traits in their learned representations that are highly entangled, leading to a lack of interpretability. In this stud…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speaker-diarizationSpeaker Diarization+2Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Recently there has been an increased interest in unsupervised learning of disentangled representations using the Variational Autoencoder (VAE) framework. Most of the existing work has focused largely on modifying the var…
DisentanglementVariational Inference