In this work we present simple grapheme-based system for low-resource speech recognition using Babel data for Turkish spontaneous speech (80 h). We have investigated different neural network architectures performance, including fully-convolutional, recurrent and ResNet with GRU. Different features and normalization techniques are compared as well. We also proposed CTC-loss modification using segmentation during training, which leads to improvement while decoding with small beam size.
Therefore, if an acoustic model is based on the neural network which estimates these posteriors, its training may be performed with a conventional error back-propagation gradient descent @cite_4 . Training of ASR system based on such a model does not require an explicit alignment of input utterance to the elements of output sequence and thus may be performed in end-to-end fashion. It is also important that CTC loss accumulates the information about the whole output sequence, and hence its optimization is in some sense an alternative to the traditional fine-tuning of neural network acoustic models by means of sequence-discriminative criteria such as sMBR @cite_17 etc. The implementation of CTC is conventionally based on RNN LSTM networks, including bidirectional ones as acoustic models, since they are known to model long context effectively.
@cite_4 @cite_17
1498436455 2114016253
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1. Acoustic models used in hidden Markov model neural-network (HMM NN) speech recognition systems are usually trained with a frame-based cross-entropy error criterion. In contrast, Gaussian mixture HMM systems are discriminatively trained using sequence-based criteria, such as minimum phone error or maximum mutual information, that are more directly related to speech recognition accuracy. This paper demonstrates that neural-network acoustic models can be trained with sequence classification criteria using exactly the same lattice-based methods that have been developed for Gaussian mixture HMMs, and that using a sequence classification criterion in training leads to considerably better performance. A neural network acoustic model with 153K weights trained on 50 hours of broadcast news has a word error rate of 34.0 on the rt04 English broadcast news test set. When this model is trained with the state-level minimum Bayes risk criterion, the rt04 word error rate is 27.7 .