The performance of Automatic Speech Recognition (ASR) is directly proportional to the quality of the corpus used and the training data quantity. Data scarcity and more children’s speech variability degrades the performance of ASR systems. As Punjabi is a tonal language and low resource language, less data is available for Punjabi children’s speech. It leads to poor ASR performance for Punjabi children speech recognition. To overcome limited data conditions, in this paper, two corpora of different domains are evaluated for testing the feasibility of ASR performance. We have implemented Tacotron as an artificial speech synthesis system for Punjabi Language. The speech audios synthesized by Tacotron are merged with available speech corpus and tested on Punjabi children ASR using Mel Frequency Cepstral Coefficients (MFCC) + pitch feature extraction, and Deep Neural Network (DNN) acoustic modeling. It is noticed that the merged data corpus has shown reduced Word Error Rate (WER) of the ASR system with a Relative Improvement (RI) of 9-12%.

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Speech recognition has been an active field of research in the last few decades since it facilitates better human–computer interaction. Native language automatic speech recognition (ASR) systems are still underdeveloped. Punjabi ASR systems are in their infancy stage because most research has been conducted only on adult speech systems; however, less work has been performed on Punjabi children’s ASR systems. This research aimed to build a prosodic feature-based automatic children speech recognition system using discriminative modeling techniques. The corpus of Punjabi children’s speech has various runtime challenges, such as acoustic variations with varying speakers’ ages. Efforts were made to implement out-domain data augmentation to overcome such issues using Tacotron-based text to a speech synthesizer. The prosodic features were extracted from Punjabi children’s speech corpus, then particular prosodic features were coupled with Mel Frequency Cepstral Coefficient (MFCC) features before being submitted to an ASR framework. The system modeling process investigated various approaches, which included Maximum Mutual Information (MMI), Boosted Maximum Mutual Information (bMMI), and feature-based Maximum Mutual Information (fMMI). The out-domain data augmentation was performed to enhance the corpus. After that, prosodic features were also extracted from the extended corpus, and experiments were conducted on both individual and integrated prosodic-based acoustic features. It was observed that the fMMI technique exhibited 20% to 25% relative improvement in word error rate compared with MMI and bMMI techniques. Further, it was enhanced using an augmented dataset and hybrid front-end features (MFCC + POV + Fo + Voice quality) with a relative improvement of 13% compared with the earlier baseline system.

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