Browsing by Author "Kathania, Hemant Kumar"

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    Data Augmentation Using Spectral Warping for Low Resource Children ASR
    (SPRINGER, 2022-12) Kathania, Hemant Kumar; Kadyan, Viredner; Kadiri, Sudarsana Reddy; Kurimo, Mikko; Dept Signal Process and Acoust; Speech Recognition; Speech Communication Technology; University of Petroleum and Energy Studies
    In low resource children automatic speech recognition (ASR) the performance is degraded due to limited acoustic and speaker variability available in small datasets. In this paper, we propose a spectral warping based data augmentation method to capture more acoustic and speaker variability. This is carried out by warping the linear prediction (LP) spectra computed from speech data. The warped LP spectra computed in a frame-based manner are used with the corresponding LP residuals to synthesize speech to capture more variability. The proposed augmentation method is shown to improve the ASR system performance over the baseline system. We have compared the proposed method with four well-known data augmentation methods: pitch scaling, speaking rate, SpecAug and vocal tract length perturbation (VTLP), and found that the proposed method performs the best. Further, we have combined the proposed method with these existing data augmentation methods to improve the ASR system performance even more. The combined system consisting of the original data, VTLP, SpecAug and the proposed spectral warping method gave the best performance by a relative word error rate reduction of 32.13% and 10.51% over the baseline system for Punjabi children and TLT-school corpus, respectively. The proposed spectral warping method is publicly available at https://github.com/kathania/Spectral-Warping.
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    Effect of Linear Prediction Order to Modify Formant Locations for Children Speech Recognition
    (2023) Kumar, Udara Laxman; Kurimo, Mikko; Kathania, Hemant Kumar; Department of Information and Communications Engineering; Dept Signal Process and Acoust; Karpov, Alexey; Samudravijaya, K.; Deepak, K. T.; Hegde, Rajesh M.; Prasanna, S. R. Mahadeva; Agrawal, Shyam S.; Speech Recognition; National Institute of Technology, Sikkim
    Children’s speech recognition shows poor performance as compared to adult speech. Large amount of data is required for the neural network models to achieve good performance. A very limited amount of children’s speech data is publicly available. A baseline system was developed using adult speech for training and children’s speech for testing. This kind of system suffers from mismatches between training and testing speech data. To overcome one of the mismatches, which is formant frequency locations between adults and children, in this paper we have explored the effect of linear prediction order to modify the formant frequency locations. The explored method studies for narrowband and wideband speech and found that they gave reductions in word error rate (WER) for GMM-HMM, DNN-HMM, and TDNN acoustic models. The TDNN acoustic model gives the best performance as compared to other acoustic models. The best formant modification factor α is 0.1 for linear prediction order 6 for narrowband speech (WER 13.82%), and α is 0.1 for linear prediction order 20 for wideband speech (WER 12.19%) for the TDNN acoustic model. Further, we have also compared the method with vocal tract length normalization (VTLN) and speaking rate adaptation (SRA), and it is found that the proposed method gives a better reduction in WERs as compared to VTLN and SRA.
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    Spectral warping based data augmentation for low resource children’s speaker verification
    (Springer, 2023-11-03) Kathania, Hemant Kumar; Kadyan, Virender; Kadiri, Sudarsana Reddy; Kurimo, Mikko; Department of Information and Communications Engineering; University of Petroleum and Energy Studies; Department of Information and Communications Engineering
    In this paper, we present our effort to develop an automatic speaker verification (ASV) system for low resources children’s data. For the children’s speakers, very limited amount of speech data is available in majority of the languages for training the ASV system. Developing an ASV system under low resource conditions is a very challenging problem. To develop the robust baseline system, we merged out of domain adults’ data with children’s data to train the ASV system and tested with children’s speech. This kind of system leads to acoustic mismatches between training and testing data. To overcome this issue, we have proposed spectral warping based data augmentation. We modified adult speech data using spectral warping method (to simulate like children’s speech) and added it to the training data to overcome data scarcity and mismatch between adults’ and children’s speech. The proposed data augmentation gives 20.46% and 52.52% relative improvement (in equal error rate) for Indian Punjabi and British English speech databases, respectively. We compared our proposed method with well known data augmentation methods: SpecAugment, speed perturbation (SP) and vocal tract length perturbation (VTLP), and found that the proposed method performed best. The proposed spectral warping method is publicly available at https://github.com/kathania/Speaker-Verification-spectral-warping .