Developed an end-to-end automatic speech recognition (ASR) pipeline Machine Translation System which takes user voice as input and converts the input into a an english Sentence of 2 primary Machine Learning Models using Keras Framework The first model is the acoustic model developed using a Bidirectional RNN + TimeDistributed Dense with Mel-Frequency Cepstral Coefficients (MFCCs) as the audio feature representation. The dataset used for training was LibriSpeech containing 1000 hours of speech derived from English audiobooks. The second is a language model that helps convert phenmes to words .
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Developed an end-to-end automatic speech recognition (ASR) pipeline Machine Translation System which takes user voice as input and converts the input into a an english Sentence of 2 primary Machine Learning Models using Keras Framework
aakash26/DeepSpeech-Neural-Network-Recognition
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Developed an end-to-end automatic speech recognition (ASR) pipeline Machine Translation System which takes user voice as input and converts the input into a an english Sentence of 2 primary Machine Learning Models using Keras Framework
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