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Children speech recognition is challenging mainly due to the inherent highvariability in children's physical and articulatory characteristics andexpressions. This variability manifests in both acoustic constructs andlinguistic usage due to the rapidly changing developmental stage in children'slife. Part of the challenge is due to the lack of large amounts of availablechildren speech data for efficient modeling. This work attempts to address thekey challenges using transfer learning from adult's models to children's modelsin a Deep Neural Network (DNN) framework for children's Automatic SpeechRecognition (ASR) task evaluating on multiple children's speech corpora with alarge vocabulary. The paper presents a systematic and an extensive analysis ofthe proposed transfer learning technique considering the key factors affectingchildren's speech recognition from prior literature. Evaluations are presentedon (i) comparisons of earlier GMM-HMM and the newer DNN Models, (ii)effectiveness of standard adaptation techniques versus transfer learning, (iii)various adaptation configurations in tackling the variabilities present inchildren speech, in terms of (a) acoustic spectral variability, and (b)pronunciation variability and linguistic constraints. Our Analysis spans over(i) number of DNN model parameters (for adaptation), (ii) amount of adaptationdata, (iii) ages of children, (iv) age dependent-independent adaptation.Finally, we provide Recommendations on (i) the favorable strategies overvarious aforementioned - analyzed parameters, and (ii) potential futureresearch directions and relevant challenges/problems persisting in DNN basedASR for children's speech.

Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations