Owen Kimball

From HMM's to segment models: a unified view of stochastic modeling for speech recognition

Many alternative models have been proposed to address some of the shortcomings of the hidden Markov model (HMM), which is currently the most popular approach to speech recognition. In particular, a variety of models that could be broadly classified as segment models have been described for representing a variable-length sequence of observation vectors in speech recognition applications. Since there are many aspects in common between these approaches, including the general recognition and training problems, it is useful to consider them in a unified framework. The paper describes a general stochastic model that encompasses most of the models proposed in the literature, pointing out similarities of the models in terms of correlation and parameter tying assumptions, and drawing analogies between segment models and HMMs. In addition, we summarize experimental results assessing different modeling assumptions and point out remaining open questions.

Context-dependent modeling for acoustic-phonetic recognition of continuous speech

This paper describes the results of our work in designing a system for phonetic recognition of unrestricted continuous speech. We describe several algorithms used to recognize phonemes using context-dependent Hidden Markov Models of the phonemes. We present results for several variations of the parameters of the algorithms. In addition, we propose a technique that makes it possible to integrate traditional acoustic-phonetic features into a hidden Markov process. The categorical decisions usually associated with heuristic acoustic-phonetic algorithms are replaced by automated training techniques and global search strategies. The combination of general spectral information and specific acoustic-phonetic features is shown to result in more accurate phonetic recognition than either representation by itself.

BYBLOS: The BBN continuous speech recognition system

In this paper, we describe BYBLOS, the BBN continuous speech recognition system. The system, designed for large vocabulary applications, integrates acoustic, phonetic, lexical, and linguistic knowledge sources to achieve high recognition performance. The basic approach, as described in previous papers [1, 2], makes extensive use of robust context-dependent models of phonetic coarticulation using Hidden Markov Models (HMM). We describe the components of the BYBLOS system, including: signal processing frontend, dictionary, phonetic model training system, word model generator, grammar and decoder. In recognition experiments, we demonstrate consistently high word recognition performance on continuous speech across: speakers, task domains, and grammars of varying complexity. In speaker-dependent mode, where 15 minutes of speech is required for training to a speaker, 98.5% word accuracy has been achieved in continuous speech for a 350-word task, using grammars with perplexity ranging from 30 to 60. With only 15 seconds of training speech we demonstrate performance of 97% using a grammar.

Integration of diverse recognition methodologies through reevaluation of N-best sentence hypotheses

This paper describes a general formalism for integrating two or more speech recognition technologies, which could be developed at different research sites using different recognition strategies. In this formalism, one system uses the N-best search strategy to generate a list of candidate sentences; the list is rescored by other systems; and the different scores are combined to optimize performance. Specifically, we report on combining the BU system based on stochastic segment models and the BBN system based on hidden Markov models. In addition to facilitating integration of different systems, the N-best approach results in a large reduction in computation for word recognition using the stochastic segment model.

Advances in transcription of broadcast news and conversational telephone speech within the combined EARS BBN/LIMSI system

This paper describes the progress made in the transcription of broadcast news (BN) and conversational telephone speech (CTS) within the combined BBN/LIMSI system from May 2002 to September 2004. During that period, BBN and LIMSI collaborated in an effort to produce significant reductions in the word error rate (WER), as directed by the aggressive goals of the Effective, Affordable, Reusable, Speech-to-text [Defense Advanced Research Projects Agency (DARPA) EARS] program. The paper focuses on general modeling techniques that led to recognition accuracy improvements, as well as engineering approaches that enabled efficient use of large amounts of training data and fast decoding architectures. Special attention is given on efforts to integrate components of the BBN and LIMSI systems, discussing the tradeoff between speed and accuracy for various system combination strategies. Results on the EARS progress test sets show that the combined BBN/LIMSI system achieved relative reductions of 47% and 51% on the BN and CTS domains, respectively

Speech recognition in multiple languages and domains: the 2003 BBN/LIMSI EARS system

We report on the results of the first evaluations for the BBN/LIMSI system under the new DARPA EARS program. The evaluations were carried out for conversational telephone speech (CTS) and broadcast news (BN) for three languages: English, Mandarin, and Arabic. In addition to providing system descriptions and evaluation results, the paper highlights methods that worked well across the two domains and those few that worked well on one domain but not the other. For the BN evaluations, which had to be run under 10 times real-time, we demonstrated that a joint BBN/LIMSI system with a time constraint achieved better results than either system alone.

Robust smoothing methods for discrete hidden Markov models

Three methods for smoothing discrete probability functions in discrete hidden Markov models for large-vocabulary continuous-speech recognition are presented. The smoothing is based on deriving a probabilistic co-occurrence matrix between the different vector-quantized spectra. Each estimated probability density is then multiplied by this matrix, ensuring that none of the probabilities are severely underestimated due to lack of training data. Experimental results show a 20-30% reduction in error rate when this smoothing is used. A word error rate of 3.0% is achieved with the DARPA 1000-word continuous speech recognition database and a word-pair grammar with a perplexity of 60.<<ETX>>

Automatic topic identification for two-level call routing

This paper presents an approach to routing telephone calls automatically, based upon their speech content. The data consist of a set of calls collected from a customer-service center with a two-level menu, which allows jumping past the second level, and we view the routing of these calls as a topic identification problem. Our topic identifier employs a multinomial model for keyword occurrences. We describe the call routing task in detail, discuss the multinomial model, and present experiments which investigate several issues that arise from using the model for this task.

Unsupervised Training on Large Amounts of Broadcast News Data

This paper presents our recent effort that aims at improving our Arabic broadcast news (BN) recognition system by using thousands of hours of un-transcribed Arabic audio in the way of unsupervised training. Unsupervised training is first carried out on the 1,900-hour English topic detection and tracking (TDT) data and is compared with the lightly-supervised training method that we have used for the DARPA EARS evaluations. The comparison shows that unsupervised training produces a 21.7% relative reduction in word error rate (WER), which is comparable to the gain obtained with light supervision methods. The same unsupervised training strategy carried out on a similar amount of Arabic BN data produces an 11.6% relative gain. The gain, though considerable, is substantially smaller than what is observed on the English data. Our initial work towards understanding the reasons for this difference is also described

The BBN BYBLOS Continuous Speech Recognition system

In this paper we describe the algorithms used in the BBN BYBLOS Continuous Speech Recognition system. The BYBLOS system uses context-dependent hidden Markov models of phonemes to provide a robust model of phonetic coarticulation. We provide an update of the ongoing research aimed at improving the recognition accuracy. In the first experiment we confirm the large improvement in accuracy that can be derived by using spectral derivative parameters in the recognition. In particular, the word error rate is reduced by a factor of two. Currently the system achieves a word error rate of 2.9% when tested on the speaker-dependent part of the standard 1000-Word DARPA Resource Management Database using the Word-Pair grammar supplied with the database. When no grammar was used, the error rate is 15.3%. Finally, we present a method for smoothing the discrete densities on the states of the HMM, which is intended to alleviate the problem of insufficient training for detailed phonetic models.