Building English ASR model with regional language support

However, using such a system much higher computation In this paper, we present a novel approach to developing an English cost than monolingual models, as both monolingual models Automatic Speech Recognition (ASR) system that can effectively must produce recognition outputs, increasing the cost of recognition handle Hindi queries, without compromising its performance for the end user. Additionally, the performance of these systems is on English. We propose a novel acoustic model (AM), referred to as sensitive to the accuracy of the LID, as the monolingual models perform SplitHead with Attention (SHA) model, features shared hidden layers well in their respective languages but suffer greatly in other across languages and language-specific projection layers combined languages. The aim of this work is not to build a truly bilingual via a self-attention mechanism. This mechanism estimates the model that can recognize both languages equally well. Instead, the weight for each language based on input data and weighs the corresponding goal is to improve the performance of the Indian English (en-IN) language-specific projection layers accordingly. Additionally, ASR model on Hindi (hi-IN) queries, bringing it reasonably close we propose a language modeling approach that interpolates to the performance of the Hindi model on Hindi queries. This is to n-gram models from both English and transliterated Hindi text corpora.