SRI's 1998 Broadcast News System - Towards Faster, Smaller, and Better Speech Recognition (original) (raw)

SRI's 1998 Broadcast News System -- Toward Faster, Better, Smaller Speech Recognition

We describe several new research directions we investigated toward the development of our broadcast news transcription system for the 1998 DARPA H4 evaluations. Our goal was to develop significantly faster and smaller speech recognition systems without degrading the word error rate of our 1997 system. We did this through significant algorithmic research creating various new techniques. A sample of these techniques was used to put together our 1998 broadcast news system, which is conceptually much simpler, faster, and smaller, but gives the same word error rate as our 1997 system. In particular, our 1998 system is based on a simple phonetically tied mixture (PTM) model with a total of only 13,000 Gaussians, as compared to a 67,000-Gaussian state-clustered system we used in 1997. 1. Introduction One of our main goals in 1998 was to significantly increase speed and decrease model size, while maintaining or improving accuracy. These goals are difficult to achieve simultaneously because ...

The development of the 1996 HTK broadcast news transcription system

1997

This paper describes our efforts in extending a large vocabulary speech recognition system to handle broadcast news transcription. Results using the 1995 DARPA H4 evaluation data set are presented for different front-end analyses and for the use of unsupervised model adaptation using maximum likelihood linear regression (MLLR). The HTK system for the 1996 H4 evaluation is then described. It includes a number of new features compared to previous HTK large vocabulary systems including decoder-guided segmentation, segment clustering, cache-based language modelling, and combined MAP and MLLR adaptation. The system makes multiple passes through the data and the detailed results of each pass are given. The overall word error rate obtained by the 1996 evaluation system was 27.5%, and a bug-fixed version reduced this to 26.6%.

The development of SRI’s 1997 Broadcast News transcription system

1998

This paper describes SRI's 1997 broadcast news transcription system used for the 1997 DARPA H4 evaluations. Our system had several novel components. These include automatic segmentation of entire broadcast shows, word-internal and crossword acoustic models robustly estimated with a new Gaussian Merging-Splitting (GMS) algorithm, the use of trigram language models (LMs) in lattices instead of for rescoring N-best lists, and an LM pruning algorithm that allows efficient representation of high-order (like 4-or 5-gram) LMs. We briefly describe these features and give comparative experimental results. We achieved a 18.7% relative improvement in performance on our 1996 H4 partitioned evaluation (PE) development test set as compared to our 1996 H4 PE evaluation system.

Transcription of broadcast news-some recent improvements to IBM's LVCSR system

1998

This paper describes extensions and improvements to IBM's large vocabulary continuous speech recognition (LVCSR) system for transcription of broadcast news. The recognizer uses an additional 35 hours of training data over the one used in the 1996 Hub4 evaluation [?]. It includes a number of new features: optimal feature space for acoustic modeling (in training and/or testing), filler-word modeling, Bayesian Information Criterion (BIC) based segment clustering, an improved implementation of iterative MLLR and 4-gram language models. Results using the 1996 DARPA Hub4 evaluation data set are presented.

The 1997 HTK Broadcast News Transcription System

1998

This paper presents the recent development of the HTK broadcast news transcription system. Previously we have used data type specific modelling based on adapted Wall Street Journal trained HMMs. However, we are now using data for which no manual preclassification or segmentation is available and therefore automatic techniques are required and compatible acoustic modelling strategies must be adopted. A number of recognition experiments are presented that compare data-type specific and non-specific models; differing amounts of training data; the use of gender-dependent modelling and the effects of automatic data-type classification. Based on these experiments, the HTK system for the 1997 broadcast news evaluation was designed. A detailed description of this system is given which includes a class-based language modelling component. The complete system yields an overall word error rate of 22.0% on the 1996 unpartitioned broadcast news development test data and just 15.8% on the 1997 evaluation test set.

The 1998 HTK Broadcast News Transcription System: Development andResults

1999

This paper presents the development of the HTK broadcast news transcription system for the November 1998 Hub4 evaluation. Relative to the previous year's system The system a number of features were added including vocal tract length normalisation; cluster-based variance normalisation; double the quantity of acoustic training data; interpolated word level language models to combine text sources; increased broadcast news language model training data; and an extra adaptation stage using a full-variance transform. Overall these changes to the system reduced the error rate by 13% on the 1997 evaluation data and the final system had an overall word error rate of 13.8% for the 1998 evaluation data sets.

Improvements in accuracy and speed in the HTK broadcast news transcription system

1999

This contribution is a report on an ongoing research aiming at the development of a speech recognition system for French, combining a standard HMM recognition tool with a syntactic parser. Because of the very high number of homophones in French and because several agreement rules spread over an unbounded number of words, we designed our GB-based morphological and syntactic parser to output a correct orthographic form from a lattice of phonemes produced by the front-head HMM recognition system. This resulting lattice of phonemes is processed by the syntactic parser, which selects the best word sequence according to its linguistic knowledge. The originality of this approach is the use of a syntactic parser, tuned to phonetic inputs, for a speech recognition task.

Progress in the CU-HTK broadcast news transcription system

IEEE Transactions on Audio, Speech & Language Processing, 2006

Broadcast News (BN) transcription has been a challenging research area for many years. In the last couple of years the availability of large amounts of roughly transcribed acoustic training data and advanced model training techniques has offered the opportunity to greatly reduce the error rate on this task. This paper describes the design and performance of BN transcription systems which make use of these developments. First the effects of using lightly-supervised training data and advanced acoustic modelling techniques are discussed. The design of a real-time broadcast news recognition system is then detailed using these new models. As system combination has been found to yield large gains in performance, a range of frameworks that allow multiple recognition outputs to be combined are next described. These include the use of multiple types of acoustic models and multiple segmentations. As a contrast a system developed by multiple sites allowing cross-site combination, the "SuperEARS" system, is also described. The various models and recognition configurations are evaluated using several recent BN development and evaluation test sets. These new BN transcription systems can give gains of over 25% relative to the CU-HTK 2003 BN system.

Acoustic modeling for the SRI Hub4 partitioned evaluation continuous speech recognition system

1997

We describe the development of the SRI system evaluated in the 1996 DARPA continuous speech recognition (CSR) Hub4 partitioned evaluation (PE). The task for the Hub4 evaluation was to recognize speech from broadcast television and radio shows. Recognizing such speech by machines poses many challenges. First, the segments to be recognized could be very long. This introduces a problem in training and recognition because of the consequentincreased system memory requirement. A simple segmentation technique is used to break long segments into shorter, more manageable lengths. The speech from broadcast news sources exhibits a variety of difficult acoustic conditions, such as spontaneous speech, band-limited speech, and speech in the presence of noise, music, or background speakers. Such background conditions lead to significant degradation in performance. We describe techniques, based on acoustic adaptation, that adapt recognition models to the different acoustic background conditions, so as to improve recognition performance. We also present a novel algorithm that clusters the test data segments so that the resulting clusters are homogeneous with respect to speakers. This is followed by acoustic adaptation to the individual clusters, resulting in a significant performance improvement. Finally, we briefly describe our studies in language modeling for the Hub4 evaluation which is detailed further in another paper in these proceedings.