  Inspired by the progress of the End-to-End approach [1], this paper
systematically studies the effects of Number of Filters of convolutional layers
on the model prediction accuracy of CNN+RNN (Convolutional Neural Networks
adding to Recurrent Neural Networks) for ASR Models (Automatic Speech
Recognition). Experimental results show that only when the CNN Number of
Filters exceeds a certain threshold value is adding CNN to RNN able to improve
the performance of the CNN+RNN speech recognition model, otherwise some
parameter ranges of CNN can render it useless to add the CNN to the RNN model.
Our results show a strong dependency of word accuracy on the Number of Filters
of convolutional layers. Based on the experimental results, the paper suggests
a possible hypothesis of Sound-2-Vector Embedding (Convolutional Embedding) to
explain the above observations.
  Based on this Embedding hypothesis and the optimization of parameters, the
paper develops an End-to-End speech recognition system which has a high word
accuracy but also has a light model-weight. The developed LVCSR (Large
Vocabulary Continuous Speech Recognition) model has achieved quite a high word
accuracy of 90.2% only by its Acoustic Model alone, without any assistance from
intermediate phonetic representation and any Language Model. Its acoustic model
contains only 4.4 million weight parameters, compared to the 35~68 million
acoustic-model weight parameters in DeepSpeech2 [2] (one of the top
state-of-the-art LVCSR models) which can achieve a word accuracy of 91.5%. The
light-weighted model is good for improving the transcribing computing
efficiency and also useful for mobile devices, Driverless Vehicles, etc. Our
model weight is reduced to ~10% the size of DeepSpeech2, but our model accuracy
remains close to that of DeepSpeech2. If combined with a Language Model, our
LVCSR system is able to achieve 91.5% word accuracy.
