Quality Assurance for Speech Synthesis with ASR

Peinl, René; Wirth, Johannes

doi:10.1007/978-3-031-16078-3_51

Cited by 2 publications

(1 citation statement)

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“…ASR is an important and useful tool for speech researchers. It forms the basis of speech understanding [11] when combined with advanced language models, but also finds applications in generative models and for training improved Text-To-Speech (TTS) models [12], [13], [14]. The interrelationship between ASR and TTS is further described in [15].…”

Section: Introductionmentioning

confidence: 99%

A WAV2VEC2-Based Experimental Study on Self-Supervised Learning Methods to Improve Child Speech Recognition

Jain,

Barcovschi,

Yiwere

et al. 2023

IEEE Access

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Despite recent advancements in deep learning technologies, Child Speech Recognition remains a challenging task. Current Automatic Speech Recognition (ASR) models require substantial amounts of annotated data for training, which is scarce. In this work, we explore using the ASR model, wav2vec2, with different pretraining and finetuning configurations for self-supervised learning (SSL) toward improving automatic child speech recognition. The pretrained wav2vec2 models were finetuned using different amounts of child speech training data, adult speech data, and a combination of both, to discover the optimum amount of data required to finetune the model for the task of child ASR. Our trained model achieves the best Word Error Rate (WER) of 7.42 on the MyST child speech dataset, 2.91 on the PFSTAR dataset and 12.77 on the CMU KIDS dataset using cleaned variants of each dataset. Our models outperformed the unmodified wav2vec2 BASE 960 on child speech using as little as 10 hours of child speech data in finetuning. The analysis of different types of training data and their effect on inference is provided by using a combination of custom datasets in pretraining, finetuning and inference. These 'cleaned' datasets are provided for use by other researchers to provide comparisons with our results.

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