Resumen
This paper describes a comparison between hybrid and end-to-end Automatic Speech Recognition (ASR) systems, which were evaluated on the IberSpeech-RTVE 2020 Speech-to-Text Transcription Challenge. Deep Neural Networks (DNNs) are becoming the most promising technology for ASR at present. In the last few years, traditional hybrid models have been evaluated and compared to other end-to-end ASR systems in terms of accuracy and efficiency. We contribute two different approaches: a hybrid ASR system based on a DNN-HMM and two state-of-the-art end-to-end ASR systems, based on Lattice-Free Maximum Mutual Information (LF-MMI). To address the high difficulty in the speech-to-text transcription of recordings with different speaking styles and acoustic conditions from TV studios to live recordings, data augmentation and Domain Adversarial Training (DAT) techniques were studied. Multi-condition data augmentation applied to our hybrid DNN-HMM demonstrated WER improvements in noisy scenarios (about 10% relatively). In contrast, the results obtained using an end-to-end PyChain-based ASR system were far from our expectations. Nevertheless, we found that when including DAT techniques, a relative WER improvement of 2.87% was obtained as compared to the PyChain-based system.