4,876 research outputs found
Improving large vocabulary continuous speech recognition by combining GMM-based and reservoir-based acoustic modeling
In earlier work we have shown that good phoneme recognition is possible with a so-called reservoir, a special type of recurrent neural network. In this paper, different architectures based on Reservoir Computing (RC) for large vocabulary continuous speech recognition are investigated. Besides experiments with HMM hybrids, it is shown that a RC-HMM tandem can achieve the same recognition accuracy as a classical HMM, which is a promising result for such a fairly new paradigm. It is also demonstrated that a state-level combination of the scores of the tandem and the baseline HMM leads to a significant improvement over the baseline. A word error rate reduction of the order of 20\% relative is possible
Subword and Crossword Units for CTC Acoustic Models
This paper proposes a novel approach to create an unit set for CTC based
speech recognition systems. By using Byte Pair Encoding we learn an unit set of
an arbitrary size on a given training text. In contrast to using characters or
words as units this allows us to find a good trade-off between the size of our
unit set and the available training data. We evaluate both Crossword units,
that may span multiple word, and Subword units. By combining this approach with
decoding methods using a separate language model we are able to achieve state
of the art results for grapheme based CTC systems.Comment: Current version accepted at Interspeech 201
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Conventional deep neural networks (DNN) for speech acoustic modeling rely on
Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary
class labels as the targets for DNN training. Subword classes in speech
recognition systems correspond to context-dependent tied states or senones. The
present work addresses some limitations of GMM-HMM senone alignments for DNN
training. We hypothesize that the senone probabilities obtained from a DNN
trained with binary labels can provide more accurate targets to learn better
acoustic models. However, DNN outputs bear inaccuracies which are exhibited as
high dimensional unstructured noise, whereas the informative components are
structured and low-dimensional. We exploit principle component analysis (PCA)
and sparse coding to characterize the senone subspaces. Enhanced probabilities
obtained from low-rank and sparse reconstructions are used as soft-targets for
DNN acoustic modeling, that also enables training with untranscribed data.
Experiments conducted on AMI corpus shows 4.6% relative reduction in word error
rate
Speech Recognition in Hindi
This project is an attempt towards reducing the gap between the computer and the people of rural India, by allowing them to use Hindi language, the most common language being used by the people in rural areas. Speech recognition will, indeed, play a very significant role in promoting the technology in the rural areas. Although many speech interfaces are already available, the need is for speech interfaces in local Indian languages, hence we attempt to build a speech recognition system in Hindi, in this
project.
The project report explains in brief about the basic model of a speech recognition engine and its different modules. It also briefs about the construction of the Hindi language dictionary and training the model for recognition of speech and finally testing the model for accuracy. The results of the tests have been provided and finally the report ends with the derived conclusion and recommended future work
- …