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Found 8 papers, 1 papers with code
Non-uniform Speaker Disentanglement For Depression Detection From Raw Speech Signals
We find that a greater adversarial weight for the initial layers leads to performance improvement.
Unsupervised Instance Discriminative Learning for Depression Detection from Speech Signals
To learn instance-spread-out embeddings, we explore methods for sampling instances for a training batch (distinct speaker-based and random sampling).
A Step Towards Preserving Speakers' Identity While Detecting Depression Via Speaker Disentanglement
With adversarial training, depression classification improves for every feature when compared to the baseline.
Automatic Dialect Density Estimation for African American English
In this paper, we explore automatic prediction of dialect density of the African American English (AAE) dialect, where dialect density is defined as the percentage of words in an utterance that contain characteristics of the non-standard dialect.
FrAUG: A Frame Rate Based Data Augmentation Method for Depression Detection from Speech Signals
The improvements for the CONVERGE (Mandarin) dataset when using the x-vector embeddings with CNN as the backend and MFCCs as input features were 9. 32% (validation) and 12. 99% (test).
Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion
We show that this simple method can improve performance on rare words by 3. 7% WER relative without degradation on general test set, and the improvement from USF is additive to any additional language model based rescoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Variable frame rate-based data augmentation to handle speaking-style variability for automatic speaker verification
For instance, when enrolled with conversation utterances, the EER increased to 3. 03%, 2. 96% and 22. 12% when tested on read, narrative, and pet-directed speech, respectively.
Exploring the Use of an Unsupervised Autoregressive Model as a Shared Encoder for Text-Dependent Speaker Verification
A fusion of the x-vector/PLDA baseline and the SID/PLDA scores prior to PID fusion further improved performance by 15% indicating complementarity of the proposed approach to the x-vector system.
