Abstract
Automatic speech recognition systems are of two types, such as monolingual and multilingual. Due to its ability to use transfer learning techniques and create better SR models for resource-scarce languages, multilingual speech recognition has recently become more prevalent. Generally, multilingual speech recognition models use specific parameters and modules which activate depending on the language given to the models. These models lack efficiency if language identity is not specified and also lacks the ability to recognize code-switch speech. In this work, we propose a multilingual model for English and Indian languages that can convert speech to text without specifying the language identity and recognize code-switch speech through transliterated and English text as input transcription. The transliterated text can help the model to learn and map sound to its appropriate English character in the case of multilingual and code-switched speech. This multilingual model uses the DeepSpeech architecture by Baidu. The lowest word error rate (WER) and character error rate (CER) for the best model were 30.5% and 11.69%, respectively.
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Priyamvada, R., Kumar, S.S., Ganesh, H.B.B., Soman, K.P. (2022). Multilingual Speech Recognition for Indian Languages. In: Gupta, D., Sambyo, K., Prasad, M., Agarwal, S. (eds) Advanced Machine Intelligence and Signal Processing. Lecture Notes in Electrical Engineering, vol 858. Springer, Singapore. https://doi.org/10.1007/978-981-19-0840-8_41
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