Using Continuous Space Language Models for Conversational Speech Recognition
نویسندگان
چکیده
Language modeling for conversational speech suffers from the limited amount of available adequate training data. This paper describes a new approach that performs the estimation of the language model probabilities in a continuous space, allowing by these means smooth interpolation of unobserved n-grams. This continuous space language model is used during the last decoding pass of a state-of-the-art conversational telephone speech recognizer to rescore word lattices. For this type of speech data, it achieves consistent word error reductions of more than 0.4% compared to a carefully tuned backoff n-gram language model.
منابع مشابه
Continuous space language models
This paper describes the use of a neural network language model for large vocabulary continuous speech recognition. The underlying idea of this approach is to attack the data sparseness problem by performing the language model probability estimation in a continuous space. Highly efficient learning algorithms are described that enable the use of training corpora of several hundred million words....
متن کاملSemi-Supervised Model Training for Unbounded Conversational Speech Recognition
For conversational large-vocabulary continuous speech recognition (LVCSR) tasks, up to about two thousand hours of audio is commonly used to train state of the art models. Collection of labeled conversational audio however, is prohibitively expensive, laborious and error-prone. Furthermore, academic corpora like Fisher English (2004) or Switchboard (1992) are inadequate to train models with suf...
متن کاملSpeaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کاملSpeaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کاملNeural network language models for conversational speech recognition
Recently there is growing interest in using neural networks for language modeling. In contrast to the well known backoff ngram language models (LM), the neural network approach tries to limit problems from the data sparseness by performing the estimation in a continuous space, allowing by these means smooth interpolations. Therefore this type of LM is interesting for tasks for which only a very...
متن کامل