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UppuluriKalyani
ananyag309
Description This project aims to build a speech recognition model that can convert spoken language (audio input) into written text. The model uses techniques from Natural Language Processing (NLP) and deep learning to process audio data and predict corresponding text. It is based on the principles of speech-to-text algorithms and Recurrent Neural Networks (RNNs).
Model Architecture The model consists of RNN layers (such as LSTM or GRU) for processing the sequence data. The final layer is a dense layer with a softmax activation for predicting the probability distribution over the vocabulary. Connectionist Temporal Classification (CTC) loss function is used to handle the alignment between input audio sequences and output text sequences.