This paper investigates the problem of updating over time the statistical language model (LM) of an Italian broadcast news transcription system. Statistical adaptation methods are proposed which try to cope with the complex dynamics of news by exploiting newswire texts daily available on the Internet. In particular, contemporary news reports are used to extend the lexicon of the LM, to minimize the out-of-vocabulary (OOV) word rate, and to adapt the n-gram probabilities. Experiments performed on 19 news shows, spanning a period of one month, showed relative reductions of 58% in OOV word rate, 16% in perplexity, and 4% in word error rate (WER
This paper describes the IBM approach to Broadcast News Transcription. Typical problems in the Broa...
Topic adaptation for language modeling is concerned with ad-justing the probabilities in a language ...
Building a stochastic language model (LM) for speech recog-nition requires a large corpus of target ...
The daily and real-time transcription of Broadcast News (BN) is a challenging task both in acoustic ...
This paper investigates the effectiveness of online temporal language model adaptation when applied ...
In this work we investigate methods to extend the lexicon of a broadcast news (BN) speech recognitio...
This paper considers dynamic language model adaptation for Mandarin broadcast news recognition. Both...
This paper presents some recent improvements in automatic transcription of Italian broadcast news ob...
This paper reports on experiments of porting the ITC-irst Italian broadcast news recognition system ...
Although the vocabularies of ASR systems are designed to achieve high coverage for the expected doma...
This paper reports on the work done on vocabulary and language model daily adaptation for a European...
This paper presents a method for n-gram language model adaptation based on the principle of minimum ...
In this paper, we present novel techniques for performing topic adaptation on an -gram language mode...
Abstract. The main goal of this work is the adaptation of a broadcast news transcription system to a...
Language modeling is an important part for both speech recognition and machine translation systems. ...
This paper describes the IBM approach to Broadcast News Transcription. Typical problems in the Broa...
Topic adaptation for language modeling is concerned with ad-justing the probabilities in a language ...
Building a stochastic language model (LM) for speech recog-nition requires a large corpus of target ...
The daily and real-time transcription of Broadcast News (BN) is a challenging task both in acoustic ...
This paper investigates the effectiveness of online temporal language model adaptation when applied ...
In this work we investigate methods to extend the lexicon of a broadcast news (BN) speech recognitio...
This paper considers dynamic language model adaptation for Mandarin broadcast news recognition. Both...
This paper presents some recent improvements in automatic transcription of Italian broadcast news ob...
This paper reports on experiments of porting the ITC-irst Italian broadcast news recognition system ...
Although the vocabularies of ASR systems are designed to achieve high coverage for the expected doma...
This paper reports on the work done on vocabulary and language model daily adaptation for a European...
This paper presents a method for n-gram language model adaptation based on the principle of minimum ...
In this paper, we present novel techniques for performing topic adaptation on an -gram language mode...
Abstract. The main goal of this work is the adaptation of a broadcast news transcription system to a...
Language modeling is an important part for both speech recognition and machine translation systems. ...
This paper describes the IBM approach to Broadcast News Transcription. Typical problems in the Broa...
Topic adaptation for language modeling is concerned with ad-justing the probabilities in a language ...
Building a stochastic language model (LM) for speech recog-nition requires a large corpus of target ...