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Typical systems for large vocabulary conversational speech recognition (LVCSR) have been trained on a few hundred hours of carefully transcribed acoustic training data. This paper describes an LVCSR system for the conversational telephone speech (CTS) task trained on more than 2000 hours of data for which only approximate transcriptions were available. The challenges of dealing which such a large data set and the accuracy improvements over the small baseline system are discussed. The effect on both acoustic and language modelling performance is studied. Overall increasing the training data size from 360h to 2200h and optimising the training procedure reduced the word error rate on the DARPA/NIST 2003 eval set by about 20% relative.

This 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 re-generation 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×RT).

In recent years, all-neural, end-to-end (E2E) ASR systems gained rapid interest in the speech recognition community. They convert speech input to text units in a single trainable Neural Network model. In ASR, many utterances contain rich named entities. Such named entities may be user or location specific and they are not seen during training. A single model makes it inflexible to utilize dynamic contextual information during inference. In this paper, we propose to train a context aware E2E model and allow the beam search to traverse into the context FST during inference. We also propose a simple method to adjust the cost discrepancy between the context FST and the base model. This algorithm is able to reduce the named entity utterance WER by 57% with little accuracy degradation on regular utterances. Although an E2E model does not need a pronunciation dictionary, its interesting to make use of existing pronunciation knowledge to improve accuracy. In this paper, we propose an algorithm to map the rare entity words to common words via pronunciation and treat the mapped words as an alternative form to the original word during recognition. This algorithm further reduces the WER on the named entity utterances by another 31%.

This paper describes the development of the 2003 CU-HTK large vocabulary speech recognition system for Conversational Telephone Speech (CTS). The system was designed based on a multipass, multi-branch structure where the output of all branches is combined using system combination. A number of advanced modelling techniques such as Speaker Adaptive Training, Heteroscedastic Linear Discriminant Analysis, Minimum Phone Error estimation and specially constructed Single Pronunciation dictionaries were employed. The effectiveness of each of these techniques and their potential contribution to the result of system combination was evaluated in the framework of a state-of-the-art LVCSR system with sophisticated adaptation. The final 2003 CU-HTK CTS system constructed from some of these models is described and its performance on the DARPA/NIST 2003 Rich Transcription (RT-03) evaluation test set is discussed.