504 research outputs found
Convolutional Neural Networks for Raw Speech Recognition
State-of-the-art automatic speech recognition (ASR) systems map the speech signal into its corresponding text. Traditional ASR systems are based on Gaussian mixture model. The emergence of deep learning drastically improved the recognition rate of ASR systems. Such systems are replacing traditional ASR systems. These systems can also be trained in end-to-end manner. End-to-end ASR systems are gaining much popularity due to simplified model-building process and abilities to directly map speech into the text without any predefined alignments. Three major types of end-to-end architectures for ASR are attention-based methods, connectionist temporal classification, and convolutional neural network (CNN)-based direct raw speech model. In this chapter, CNN-based acoustic model for raw speech signal is discussed. It establishes the relation between raw speech signal and phones in a data-driven manner. Relevant features and classifier both are jointly learned from the raw speech. Raw speech is processed by first convolutional layer to learn the feature representation. The output of first convolutional layer, that is, intermediate representation, is more discriminative and further processed by rest convolutional layers. This system uses only few parameters and performs better than traditional cepstral feature-based systems. The performance of the system is evaluated for TIMIT and claimed similar performance as MFCC
A hypothesize-and-verify framework for Text Recognition using Deep Recurrent Neural Networks
Deep LSTM is an ideal candidate for text recognition. However text
recognition involves some initial image processing steps like segmentation of
lines and words which can induce error to the recognition system. Without
segmentation, learning very long range context is difficult and becomes
computationally intractable. Therefore, alternative soft decisions are needed
at the pre-processing level. This paper proposes a hybrid text recognizer using
a deep recurrent neural network with multiple layers of abstraction and long
range context along with a language model to verify the performance of the deep
neural network. In this paper we construct a multi-hypotheses tree architecture
with candidate segments of line sequences from different segmentation
algorithms at its different branches. The deep neural network is trained on
perfectly segmented data and tests each of the candidate segments, generating
unicode sequences. In the verification step, these unicode sequences are
validated using a sub-string match with the language model and best first
search is used to find the best possible combination of alternative hypothesis
from the tree structure. Thus the verification framework using language models
eliminates wrong segmentation outputs and filters recognition errors
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