Statistical acoustic model adaptation method, acoustic model learning method suitable for statistical acoustic model adaptation, storage medium storing parameters for building deep neural network, and computer program for adapting statistical acoustic model

An acoustic model learning method suitable for statistical acoustic model adaptation under a specific condition, wherein

• said acoustic model is an acoustic model using Deep Neural Network or DNN, said DNN including a plurality of layers of three or more;
• said method comprising the steps of:
• a computer readable first storage device separately storing speech data under a plurality of conditions;
• a computer preparing a plurality of hidden layer modules for respective conditions corresponding to said plurality of conditions;
• the computer, while switching and selecting speech data under said plurality of conditions, performing preliminary learning of all layers of said DNN while dynamically replacing a specific one of said plurality of layers with a hidden layer module corresponding to the selected speech data;
• and
• the computer storing, in a storage medium, parameters of layers other than said specific layer of said DNN that has completed learning at said step of preliminary learning.

A storage medium storing parameters for building a Deep Neural Network or DNN for an acoustic model learning, suitable for statistical acoustic model adaptation for speech recognition under a specific condition, wherein

• the parameters stored in said storage medium are for building a DNN including a plurality of layers of three or more;
• said storage medium further storing
• a program for training, by fixing parameters of layers other than a specific layer of the DNN built by using the parameters stored in said storage medium, said DNN with speech data, whereby training only said specific layer of said DNN;
• wherein
• said specific layer is predetermined corresponding to the DNN stored in said storage medium.

A computer program for adapting a statistical acoustic model for speech recognition under a specific condition, wherein

• said acoustic model is an acoustic model using Deep Neural Network or DNN, said DNN including a plurality of layers of three or more;
• said computer program causing a computer to execute the steps of:
• separately storing, from a computer readable first storage device, speech data under a plurality of conditions;
• preparing a plurality of hidden layer modules for respective conditions corresponding to said plurality of conditions;
• while switching and selecting speech data under said plurality of conditions, performing preliminary learning of all layers of said DNN while dynamically replacing a specific one of said plurality of layers with a hidden layer module corresponding to the selected speech data;
• replacing said specific layer of said DNN that has completed the learning at said step of performing preliminary learning with an initial hidden layer prepared in advance;
• and
• storing the parameters of DNN obtained at said step of replacing in a storage medium.