Automatic Transcription Of Broadcast News Research Articles

Speech/music segmentation using entropy and dynamism features in a HMM classification framework

In this paper, we present a new approach towards high performance speech/music discrimination on realistic tasks related to the automatic transcription of broadcast news. In the approach presented here, an artificial neural network (ANN) trained on clean speech only (as used in a standard large vocabulary speech recognition system) is used as a channel model at the output of which the entropy and “dynamism” will be measured every 10 ms. These features are then integrated over time through an ergodic 2-state (speech and non-speech) hidden Markov model (HMM) with minimum duration constraints on each HMM state. For instance, in the case of entropy, it is indeed clear (and observed in practice) that, on average, the entropy at the output of the ANN will be larger for non-speech segments than speech segments presented at their input. In our case, the ANN acoustic model was a multi-layer perceptron (MLP, as often used in hybrid HMM/ANN systems) generating at its output estimators of the phonetic posterior probabilities based on the acoustic vectors at its input. It is from these outputs, thus from “real” probabilities, that the entropy and dynamism are estimated. The 2-state speech/non-speech HMM will take these two-dimensional features (entropy and dynamism) whose distributions will be modeled through multi-Gaussian densities or a secondary MLP. The parameters of this HMM are trained in a supervised manner using Viterbi algorithm. Although the proposed method can easily be adapted to other speech/non-speech discrimination applications, the present paper only focuses on speech/music segmentation. Different experiments, including different speech and music styles, as well as different temporal distributions of the speech and music signals (real data distribution, mostly speech, or mostly music), illustrate the robustness of the approach, always resulting in a correct segmentation performance higher than 90%. Finally, we will show how a confidence measure can be used to further improve the segmentation results, and also discuss how this may be used to extend the technique to the case of speech/music mixtures.

Progress in transcription of Broadcast News using Byblos

In this paper, we describe our progress during the last four years (1995–1999) in automatic transcription of broadcast news from radio and television using the BBN Byblos speech recognition system. Overall, we achieved steady progress as reflected through the results of the last four DARPA Hub-4 evaluations, with word error rates of 42.7%, 31.8%, 20.4% and 14.7% in 1995, 1996, 1997 and 1998, respectively. This progress can be attributed to improvements in acoustic modeling, channel and speaker adaptation, and search algorithms, as well as dealing with specific characteristics of the real-life variable speech found in broadcast news. Besides improving recognition accuracy, we also succeeded in developing several algorithms to achieve close-to-real-time recognition speed without a significant sacrifice in recognition accuracy.

Automatic transcription of Broadcast News

This paper describes the IBM approach to Broadcast News (BN) transcription. Typical problems in the BN transcription task are segmentation, clustering, acoustic modeling, language modeling and acoustic model adaptation. This paper presents new algorithms for each of these focus problems. Some key ideas include Bayesian information criterion (BIC) (for segmentation, clustering and acoustic modeling) and speaker/cluster adapted training (SAT/CAT).

Improved modeling and efficiency for automatic transcription of Broadcast News

Over the last few years, the DARPA-sponsored Hub-4 continuous speech recognition evaluations have advanced speech recognition technology for automatic transcription of broadcast news. In this paper, we report on our research and progress in this domain, with an emphasis on efficient modeling with significantly fewer parameters for faster and more accurate recognition. In the acoustic modeling area, this was achieved through new parameter tying, Gaussian clustering, and mixture weight thresholding schemes. The effectiveness of acoustic adaptation is greatly increased through unsupervised clustering of test data. In language modeling, we explored the use of non-broadcast-news training data as well as the adaptation to topic and speaking styles. We developed an effective and efficient parameter pruning technique for backoff language models that allowed us to cope with ever increasing amounts of training data and expanded N-gram scopes. Finally, we improved our progressive search architecture with more efficient algorithms for lattice generation, compaction, and incorporation of higher-order language models.