Combined approach to dysarthric speaker verification using data augmentation and feature fusion

In this study, the challenges of adapting automatic speaker verification (ASV) systems to accommodate individuals with dysarthria, a speech disorder affecting intelligibility and articulation, are addressed. The scarcity of dysarthric speech data presents a significant obstacle in the development of an effective ASV system. To mitigate the detrimental effects of data paucity, an out-of-domain data augmentation approach was employed based on the observation that dysarthric speech often exhibits longer phoneme duration. Motivated by this observation, the duration of healthy speech data was modified with various stretching factors and then pooled into training, resulting in a significant reduction in the error rate. In addition to analyzing average phoneme duration, another analysis revealed that dysarthric speech contains crucial high-frequency spectral information. However, Mel-frequency cepstral coefficients (MFCC) are inherently designed to down-sample spectral information in the higher-frequency regions, and the same is true for Mel-filterbank features. To address this shortcoming, Linear-filterbank cepstral coefficients (LFCC) were used in combination with MFCC features. While MFCC effectively captures certain aspects of dysarthric speech, LFCC complements this by capturing high-frequency details essential for accurate dysarthric speaker verification. This proposed feature fusion effectively minimizes spectral information loss, further reducing error rates. To support the significance of combination of MFCC and LFCC features in an automatic speaker verification system for speakers with dysarthria, comprehensive experimentation was conducted. The fusion of MFCC and LFCC features was compared with several other front-end acoustic features, such as Mel-filterbank features, linear filterbank features, wavelet filterbank features, linear prediction cepstral coefficients (LPCC), frequency domain LPCC, and constant Q cepstral coefficients (CQCC). The approaches were evaluated using both i-vector and x-vector-based representation, comparing systems developed using MFCC and LFCC features individually and in combination. The experimental results presented in this paper demonstrate substantial improvements, with a 25.78% reduction in equal error rate (EER) for i-vector models and a 23.66% reduction in EER for x-vector models when compared to the baseline ASV system. Additionally, the effect of feature concatenation with variation in dysarthria severity levels (low, medium, and high) was studied, and the proposed approach was found to be highly effective in those cases as well.