Add to ORKGIncluding phrases in the vocabulary list can improve n-gram language models used in speech recognition. In this paper, we report results of automatic extraction of phrases from the training text using frequency, likelihood, and correlation criteria. We show how a language model built from a vocabulary that includes useful phrases can systematically improve language model perplexity in a natural language call-routing task and the 20K-Nov92 Wall Street Journal evaluation. We also discuss the impact of such phrase-based language models on recognition word error rate
Language models for speech recognition tend to concentrate solely on recognizing the words that were...
The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands...
We attemped to improve recognition accuracy by reduc-ing the inadequacies of the lexicon and languag...
Statistical language models (SLM) encode linguistic information in the form of estimation of probabi...
Article dans revue scientifique avec comité de lecture.In natural language and especially in spontan...
Introduction At the current state of the art, high-accuracy speech recognition with moderate to lar...
It seems obvious that a successful model of natural language would incorporate a great deal of both ...
Colloque avec actes et comité de lecture. internationale.International audienceThis paper focuses on...
In natural languages multiple word sequences can represent the same underlying meaning. Only modelli...
Language models are an important component of speech recognition. They aim to predict the probabilit...
One particular problem in large vocabulary continuous speech recognition for low-resourced languages...
Abstract We present a modification of the traditional n-gram language modeling approach that departs...
The most widely-used evaluation metric for language models for speech recognition is the perplexity ...
Since the advent of deep learning, automatic speech recognition (ASR), like many other fields, has a...
[[abstract]]N-gram language modeling is a crucial component in any speech recognizer since it is exp...
Language models for speech recognition tend to concentrate solely on recognizing the words that were...
The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands...
We attemped to improve recognition accuracy by reduc-ing the inadequacies of the lexicon and languag...
Statistical language models (SLM) encode linguistic information in the form of estimation of probabi...
Article dans revue scientifique avec comité de lecture.In natural language and especially in spontan...
Introduction At the current state of the art, high-accuracy speech recognition with moderate to lar...
It seems obvious that a successful model of natural language would incorporate a great deal of both ...
Colloque avec actes et comité de lecture. internationale.International audienceThis paper focuses on...
In natural languages multiple word sequences can represent the same underlying meaning. Only modelli...
Language models are an important component of speech recognition. They aim to predict the probabilit...
One particular problem in large vocabulary continuous speech recognition for low-resourced languages...
Abstract We present a modification of the traditional n-gram language modeling approach that departs...
The most widely-used evaluation metric for language models for speech recognition is the perplexity ...
Since the advent of deep learning, automatic speech recognition (ASR), like many other fields, has a...
[[abstract]]N-gram language modeling is a crucial component in any speech recognizer since it is exp...
Language models for speech recognition tend to concentrate solely on recognizing the words that were...
The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands...
We attemped to improve recognition accuracy by reduc-ing the inadequacies of the lexicon and languag...