Subspace Gaussian Mixture Modeling for low-resource non-native Punjabi Language Speech Recognition

The advancement of non-native recognition of speech is becoming more significant as individual research interest in communicating with various languages has developed. However, because of the limited ability to include semantic level contextual signals for non-native language, the resilience and performance of Automatic Speech Recognition (ASR) systems have been degraded. The objectives of this research include the collection and pre-processing of speech data, the training of an acoustic model using Gaussian Mixture Models (GMMs), the development of a Punjabi-specific language model, and the creation of a comprehensive pronunciation dictionary. Similarly, the performance of proposed ASR system has been evaluated using relevant measures that consider the unique characteristics of excellent non-native Punjabi speakers’ speech. Furthermore, three three distinct kinds of language models using a combined subspace Gaussian Mixture Modeling and Hidden Markov Modeling (sGMM-HMM) technique has been employed with an objective of tackling the issue of shared-state parameters due to a limited dataset. Overall, the findings obtained on sGMM-HMM yielded a Word Error Rate (WER) of 16.41%, which appears respectable in contrast to the native language ASR system.