Context-Dependent Classes in a Hybrid Recurrent Network-HMM Speech Recognition System
نویسندگان
چکیده
A method for incorporating context-dependent phone classes in a connectionist-HMM hybrid speech recognition system is introduced. A modular approach is adopted, where single-layer networks discriminate between different context classes given the phone class and the acoustic data. The context networks are combined with a context-independent (CI) network to generate context-dependent (CD) phone probability estimates. Experiments show an average reduction in word error rate of 16% and 13% from the CI system on ARPA 5,000 word and SQALE 20,000 word tasks respectively. Due to improved modelling, the decoding speed of the CD system is more than twice as fast as the CI system.
منابع مشابه
A new hybrid system based on MMI-neural networks for the RM speech recognition task
We present a hybrid speech recognition system for speaker independent continuous speech recognition. The system combines a novel information theory based neural network (NN) paradigm and discrete Hidden Markov models (HMMs) including State-of-the-Art techniques like state clustered triphones. The novel NN type is trained by an algorithm based on principles of self-organization that achieves max...
متن کاملHybrid neural network/hidden Markov model continuous-speech recognition
n M In this paper we present a hybrid multilayer perceptron (MLP)/hidde arkov model (HMM) speaker-independent continuous-speech recognib tion system, in which the advantages of both approaches are combined y using MLPs to estimate the state-dependent observation probabilities p of an HMM. New MLP architectures and training procedures are resented which allow the modeling of multiple distributio...
متن کاملLarge vocabulary speech recognition with context dependent MMI-connectionist / HMM systems using the WSJ database
In this paper we present a context dependent hybrid MMI-connectionist / Hidden Markov Model (HMM) speech recognition system for the Wall Street Journal (WSJ) database. The hybrid system is build with a neural network, which is used as a vector quantizer (VQ) and an HMM with discrete probablility density functions, which has the advantage of a faster decoding. The neural network is trained on an...
متن کامل— — — — — — — — — A New Approach to Hybrid HMM / ANN Speech
This paper presents a new approach to speech recognition with hybrid HMM/ANN technology. While the standard approach to hybrid HMM/ANN systems is based on the use of neural networks as posterior probability estimators, the new approach is based on the use of mutual information neural networks trained with a special learning algorithm in order to maximize the mutual information between the input...
متن کاملContext-dependent hybrid HME/HMM speech recognition using polyphone clustering decision trees
This paper presents a context-dependent hybrid connectionist speech recognition system that uses a set of generalized hierarchical mixtures of experts (HME) to estimate context-dependent posterior acoustic class probabilities. The connectionist part of the system is organized in a modular fashion, allowing the distributed training of such a system on regular workstations. Context classes are ba...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1995