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When it comes to speech recognition for voice search, it would be
advantageous to take into account application information associated
with speech queries. However, in practice, the vast majority
of queries typically lack such annotations, posing a challenge to
train domain-specific language models (LMs). To obtain robust domain
LMs, typically a LM which has been pre-trained on general
data will be adapted to specific domains. We propose four adaptation
schemes to improve the domain performance of long shortterm

In the conventional frame feature based music genre
classification methods, the audio data is represented by
independent frames and the sequential nature of audio is totally
ignored. If the sequential knowledge is well modeled and
combined, the classification performance can be significantly
improved. The long short-term memory(LSTM) recurrent
neural network (RNN) which uses a set of special memory
cells to model for long-range feature sequence, has been
successfully used for many sequence labeling and sequence