| dc.description.abstract | Extensive use of voice assistants by children in their day-to-day life activities asks for better performance of Automatic Speech Recognition (ASR) for children’speech. The recent advancements in ASR perform better for adult speech. However, due to acoustic mismatch (in particular, higher pitch frequency and thus, the poor spectral resolution and less availability of children data), it remains a challenge to improve the performance of children’s ASR. Due to less availability of children’s speech data to train the deep neural network, data augmentation is one of the key research areas for children’s ASR. This thesis explores well known data augmentation approaches from the literature, i.e., audio (speed and tempo) perturbation and SpecAugment methods. In the thesis, the voice conversion-based data augmentation technique using a Cycleconsistent Generative Adversarial Network (CycleGAN) is proposed for hybrid DNN-HMM and end-to-end (E2E) ASR systems. Here, CycleGAN training is exploited for children-to-children voice conversion for hybrid DNN-HMM ASR and adult-to-children voice conversion for E2E ASR systems. The performance comparison with and without data augmentation is presented for different augmentation strategies. ASR experiments were performed using the children ASR corpora released in INTERSPEECH challenges. The effect of using out-of-domain data for data augmentation is observed, in particular, for male-to-children class and female-to-children class voice conversion. Both the approaches performed well with a significant reduction in word error rate (WER) of the children’s ASR system. Another application of the proposed CycleGAN architecture is investigated in the voice privacy system, where male-to-female and female-to-male class mapping is obtained to modify the speaker-specific information. Thus, providing a good anonymization method. | |
