A review on speech recognition approaches and challenges for Portuguese: exploring the feasibility of fine-tuning large-scale end-to-end models - Yan Li; Yapeng Wang; Lap Man Hoi; Dingcheng Yang; Sio-Kei Im (EURASIP Journal on Audio, Speech, and Music Processing)
Abstract At present, automatic speech recognition has become an important bridge for human-computer interaction and is widely applied in multiple fields. The Portuguese speech recognition task is gradually receiving attention due to its unique language stance. However, the relatively scarce data resources have constrained the development and application of Portuguese speech recognition systems. The neglect of accent issues is also detrimental to the promotion of recognition systems. This study focuses on the research progress of end-to-end technology on Portuguese speech recognition task. It discusses relevant research from two directions: Brazilian Portuguese recognition and European Portuguese recognition, and organizes available corpus resources for potential researchers. Then, taking European Portuguese speech recognition as an example, it takes the Fairseq-S2T and Whisper as benchmarks tested on a 500-h European Portuguese dataset to estimate the performance of large-scale pre-trained models and fine-tuning techniques. Whisper obtained a WER of 5.11% which indicates that multilingual joint training can enhance the generalization ability. Finally, to the existing problems in Portuguese speech recognition, it explores future research directions, which provides new ideas for the next stage of research and system construction.