Topic
Word error rate
About: Word error rate is a research topic. Over the lifetime, 11939 publications have been published within this topic receiving 298031 citations.
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TL;DR: Two new context-dependent phonetic units are introduced: function-word-dependent phone models, which focus on the most difficult subvocabulary; and generalized triphones, which combine similar triphones on the basis of an information-theoretic measure.
Abstract: Context-dependent phone models are applied to speaker-independent continuous speech recognition and shown to be effective in this domain. Several previously proposed context-dependent models are evaluated, and two new context-dependent phonetic units are introduced: function-word-dependent phone models, which focus on the most difficult subvocabulary; and generalized triphones, which combine similar triphones on the basis of an information-theoretic measure. The subword clustering procedure used for generalized triphones can find the optimal number of models, given a fixed amount of training data. It is shown that context-dependent modeling reduces the error rate by as much as 60%. >
228 citations
IBM1
TL;DR: In this paper, a language generator for a speech recognition apparatus scores a word-series hypothesis by combining individual scores for each word in the hypothesis, and the hypothesis score for a single word comprises a combination of the estimated conditional probability of occurrence of a first class of words comprising the word being scored, given the occurrence of the context comprising the words in the word series hypothesis other than the word was being scored.
Abstract: A language generator for a speech recognition apparatus scores a word-series hypothesis by combining individual scores for each word in the hypothesis. The hypothesis score for a single word comprises a combination of the estimated conditional probability of occurrence of a first class of words comprising the word being scored, given the occurrence of a context comprising the words in the word-series hypothesis other than the word being scored, and the estimated conditional probability of occurrence of the word being scored given the occurrence of the first class of words, and given the occurrence of the context. An apparatus and method are provided for classifying multiple series of words for the purpose of obtaining useful hypothesis scores in the language generator and speech recognition apparatus.
227 citations
25 Aug 2013
TL;DR: It is found that with randomly initialized weights, the squared error based ANN does not converge to a good local optimum, and with a good initialization by pre-training, the word error rate of the best CE trained system could be reduced.
Abstract: In this paper we investigate the error criteria that are optimized during the training of artificial neural networks (ANN). We compare the bounds of the squared error (SE) and the crossentropy (CE) criteria being the most popular choices in stateof-the art implementations. The evaluation is performed on automatic speech recognition (ASR) and handwriting recognition (HWR) tasks using a hybrid HMM-ANN model. We find that with randomly initialized weights, the squared error based ANN does not converge to a good local optimum. However, with a good initialization by pre-training, the word error rate of our best CE trained system could be reduced from 30.9% to 30.5% on the ASR, and from 22.7% to 21.9% on the HWR task by performing a few additional “fine-tuning” iterations with the SE criterion.
226 citations
TL;DR: This paper introduces a neural network architecture, which performs multichannel filtering in the first layer of the network, and shows that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target Speaker direction.
Abstract: Multichannel automatic speech recognition (ASR) systems commonly separate speech enhancement, including localization, beamforming, and postfiltering, from acoustic modeling. In this paper, we perform multichannel enhancement jointly with acoustic modeling in a deep neural network framework. Inspired by beamforming, which leverages differences in the fine time structure of the signal at different microphones to filter energy arriving from different directions, we explore modeling the raw time-domain waveform directly. We introduce a neural network architecture, which performs multichannel filtering in the first layer of the network, and show that this network learns to be robust to varying target speaker direction of arrival, performing as well as a model that is given oracle knowledge of the true target speaker direction. Next, we show how performance can be improved by factoring the first layer to separate the multichannel spatial filtering operation from a single channel filterbank which computes a frequency decomposition. We also introduce an adaptive variant, which updates the spatial filter coefficients at each time frame based on the previous inputs. Finally, we demonstrate that these approaches can be implemented more efficiently in the frequency domain. Overall, we find that such multichannel neural networks give a relative word error rate improvement of more than 5% compared to a traditional beamforming-based multichannel ASR system and more than 10% compared to a single channel waveform model.
221 citations