Neural Networks Weights and bias

Audio data conversion
weight or bias value

Some people grab audio input in one format (FLV let say) and then convert in into WAV to run against mature Sphix4 language model that developed for WAV format (Voxforge model for instance). It is strongly recommended to avoid such way of using. Conversion into WAV could produce malfunction of recognition engine.

At first stage of recognition Sphinx4 transforms audio data into feature vector and then into phonemes using acoustic model.

{DATA} -> [Sphinx4 Front End] -> {Feature Vector} -> {Phonemes}

Acoustic model is done using concrete file format and parameters such as sampling rate etc. It is recommended to put in recognitions engine original audio data of the same format.

Conversion of format, changing in sample rate can cause corruption of original data. Converted sound can be still good for human ears but can miss some important information for recognition.

Influence of conversion was studied an a few papers.

“A video stored on a media website such as YouTube has undergone a conversion from its original format to a compressed format, e.g. Flash Video (FLV). It is then retrieved for the experiment purpose and converted in suitable WAV format. However, vital information has been lost during the compression and quality of the final WAV audio is nearly equivalent to the compressed one. Besides, the input file should be of same format and quality as that with which the acoustic model was trained. Possibilities of getting interesting results using HUB4 to decode compressed news videos is thus low. A solution would be to train an acoustic model using FLV encoded samples.” (Boris Guenebaut ‘Automatic Subtitle Generation for Sound in Videos’)

Source: SmallSoftwareFactory - Java, J2EE development, speech and pattern recognition, neural networks

Neural Networks: A Tutorial
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