Bag of Models Based Embeddings for Assessment of Neurological Disorders Using Speech Intelligibility

Chandrakala, S.; Malini, S.; Jayalakshmi, S.

doi:10.1109/tetc.2020.3003085

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…We use speaker embeddings extracted from a pre-trained x-vector system [23] and apply principal component analysis (PCA) to reduce the dimensionality of these x-vectors from 512 to 32 to avoid overfitting. x-vectors have previously been used to estimate speech intelligibility [24] due to their ability to encode many properties about a speaker (i.e. speaking style, speaking rate, etc.)…”

Section: Datamentioning

confidence: 99%

Aphasic Speech Recognition Using a Mixture of Speech Intelligibility Experts

Perez¹,

Aldeneh²,

Provost³

2020

Interspeech 2020

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Robust speech recognition is a key prerequisite for semantic feature extraction in automatic aphasic speech analysis. However, standard one-size-fits-all automatic speech recognition models perform poorly when applied to aphasic speech. One reason for this is the wide range of speech intelligibility due to different levels of severity (i.e., higher severity lends itself to less intelligible speech). To address this, we propose a novel acoustic model based on a mixture of experts (MoE), which handles the varying intelligibility stages present in aphasic speech by explicitly defining severity-based experts. At test time, the contribution of each expert is decided by estimating speech intelligibility with a speech intelligibility detector (SID). We show that our proposed approach significantly reduces phone error rates across all severity stages in aphasic speech compared to a baseline approach that does not incorporate severity information into the modeling process.

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Section: Datamentioning

confidence: 99%

Aphasic Speech Recognition Using a Mixture of Speech Intelligibility Experts

Perez¹,

Aldeneh²,

Provost³

2020

Interspeech 2020

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“…Srinivasan et al [56] proposed a multi-view representation-based disordered speech recognition system based on auditory image-based features and cepstral characteristics, showing improved performance in recognizing very low intelligibility words compared to conventional methods. Chandrakala et al [57] presented a bag-ofmodels (BoM)-based approach that uses adjusted Gaussian mixture model (AGMM)-based embeddings for impaired speech-intelligibility evaluation. They tested the method on two datasets and discovered that it outperformed the supervector, hybrid GMM/SVM, i-vector, and x-vector-based techniques in terms of prediction error and reliability for intelligibilitylevel evaluation and score predictions.…”

Section: Assessing Speech-signal Impairmentsmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

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Nonintrusive objective measurement of speech intelligibility: A review of methodology

Feng

Chen

2022

Biomedical Signal Processing and Control

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Bag of Models Based Embeddings for Assessment of Neurological Disorders Using Speech Intelligibility

Cited by 3 publications

References 30 publications

Aphasic Speech Recognition Using a Mixture of Speech Intelligibility Experts

Aphasic Speech Recognition Using a Mixture of Speech Intelligibility Experts

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Nonintrusive objective measurement of speech intelligibility: A review of methodology

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