Do Yeong Kim

The Cu-Htk Mandarin Broadcast News Transcription System

This paper discusses the development of the CU-HTK Mandarin broadcast news (BN) transcription system. The Mandarin BN task includes a significant amount of English data. Hence techniques have been investigated to allow the same system to handle both Mandarin and English by augmenting the Mandarin training sets with English acoustic and language model training data. A range of acoustic models were built including models based on Gaussianised features, speaker adaptive training and feature-space MPE. A multi-branch system architecture is described in which multiple acoustic model types, alternate phone sets and segmentations can be used in a system combination framework to generate the final output. The final system shows state-of-the-art performance over a range of test sets

Recent advances in broadcast news transcription

Th paper describes recent advances in the CU-HTK Broadcast News English (BN-E) transcription system and its performance in the DARPA/NIST Rich Transcription 2003 Speech-to-Text (RT-03) evaluation. Heteroscedastic linear discriminant analysis (HLDA) and discriminative training, which were previously developed in the context of the recognition of conversational telephone speech, have been successfully applied to the BN-E task for the first time. A number of new features have also been added. These include gender-dependent (GD) discriminative training and modified discriminative training using lattice regeneration and combination. On the 2003 evaluation set, the system gave an overall word error rate of 10.7% in less than 10 times real time (10/spl times/RT).

Development of the CU-HTK 2004 broadcast news transcription systems

The paper describes our recent work on improving broadcast news transcription and presents details of the CU-HTK broadcast news English (BN-E) transcription system for the DARPA/NIST rich transcription 2004 speech-to-text (RT04) evaluation. A key focus has been building a system using an order of magnitude more acoustic training data than we have previously attempted. We have also investigated a range of techniques to improve both minimum phone error (MPE) training and the efficient creation of MPE-based narrow-band models. The paper describes two alternative system structures that run in under 10/spl times/RT and a further system that runs in less than 1/spl times/RT. This final system gives lower word error rates than our 2003 system that ran in 10/spl times/RT.