An Efficient English-to-Hindi Speech Translation System Using Bi-LSTM

The process of translating speech from English to Hindi is fraught with several challenges. These include intricate linguistic nuances and diverse sentence structures that make the translation process complex. Additionally, there are significant phonetic variations that further complicate the task. The complexities of intermediary processes involved in translation add another layer of difficulty. Moreover, there is a scarcity of parallel corpora, which are essential for training and refining translation systems. Finally, the need to handle dialectal variations and diverse accents within the Hindi language presents additional challenges for an English-to-Hindi speech translation system. This paper introduces an English-to-Hindi speech translation system employing a Bidirectional Long Short-Term Memory (Bi-LSTM). The proposed framework eliminates intermediary processes such as speech detection and separate translation steps, streamlining the entire translation pipeline. The Bi-LSTM model has been trained explicitly for translating English speech into coherent Hindi, adept at capturing intricate linguistic nuances and contextual information. Furthermore, the integration of CNN modules augments the model's ability to extract hierarchical features from input speech representations. Leveraging the capabilities of deep learning, our system comprehensively understands English speech patterns and generates fluent and coherent Hindi translations directly.