Automatic Speech Recognition in Portuguese Applied to Radio Communication
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Data
2024-03-06
Autores
Scart, Lucas Grigoleto
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Universidade Federal do Espírito Santo
Resumo
Speech is the main form of communication used between humans, and as such understanding spoken language is one of the main goals of natural language processing. Automatic speech recognition, the focus of this work, is the ability of a machine to recognize the content of words and sentences in a spoken language and transform them into a textual format. Currently, methods based on deep neural networks have dominated the field of speech processing, presenting state-of-the-art results in multiple applications. As the field of speech recognition continues to evolve, several challenges arise when attempting to adapt models to new languages and datasets, particularly in the context of radio communication recordings, as presented in this study. Compared to English, Portuguese has less available annotated speech data, making it essential to explore methods for effectively utilizing unlabeled data during training. Additionally, radio communication recordings exhibit a substantial degree of variation in background noise and speaker characteristics compared to other audio datasets. This variability can affect the accuracy and robustness of the model. This study proposes utilizing out-of-domain annotated data through a data augmentation method to build baseline models. In addition, we explore the effective use of unlabeled in-domain data via self-training techniques by generating pseudo-labels. Finally, we present an efficient training recipe for scaling large model finetuning while minimizing computational costs. Those models were then deployed as part of a broader speech processing application that was developed to assist in the auditing process of recorded railway communications. When performing the training with the simulated data, it is was observed a relative reduction of 51.7% in the character error rate considering the most challenging noise level (SNR of 0 dB), with a similar decrease at all noise levels when compared with the vanilla model. With self-training using in-domain data, we observe a reduction of 63.8% in character error rate when compared to the baseline model. We hope that the methodology developed in this work may open space to develop more robust speech recognition models with future applications in radio communication.
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Reconhecimento de fala , redes neurais profundas