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A discriminative training algorithm for hidden Markov models
- IEEE Trans. Speech Audio Proc.,vol
, 2004
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Building an ASR System for Noisy Environments: SRI's 2001 SPINE Evaluation System
- Proceedings of the International Conference on Spoken Language Processing
, 2002
"... We describe SRI's recognition system as used in the 2001 DARPA Speech in Noisy Environments (SPINE) evaluation. The SPINE task involves recognition of speech in simulated military environments. The task had some unique challenges, including segmentation of foreground speech from noisy backgroun ..."
Abstract
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Cited by 6 (1 self)
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We describe SRI's recognition system as used in the 2001 DARPA Speech in Noisy Environments (SPINE) evaluation. The SPINE task involves recognition of speech in simulated military environments. The task had some unique challenges, including segmentation of foreground speech from noisy background, the need for robust acoustic models to handle noisy speech, and development of language models from limited training data. In developing the SRI evaluation system for this task, we addressed each of these challenges using a combination of state-of-the-art techniques, including several types of feature normalization, model adaptation, class-based language modeling, multi-pass segmentation and recognition, and word posterior-based decoding and system combination 1.
Progress on mandarin conversational telephone speech recognition
- in International Symposium on Chinese Spoken Language Processing
, 2004
"... Over the past decade, there has been good progress on English conversational telephone speech (CTS) recognition, built on the Switchboard and Fisher corpora. In this paper, we present our efforts on extending language-independent technologies into Mandarin CTS, as well as addressing language-depende ..."
Abstract
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Cited by 1 (1 self)
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Over the past decade, there has been good progress on English conversational telephone speech (CTS) recognition, built on the Switchboard and Fisher corpora. In this paper, we present our efforts on extending language-independent technologies into Mandarin CTS, as well as addressing language-dependent issues such as tone. We will show the impact of each of the following factors: (a) simplified Mandarin phone set, (b) pitch features, (c) auto-retrieved web texts for augmenting ngram training, (d) speaker adaptive training, (e) maximum mutual information estimation, (f) decision-tree-based parameter sharing, (g) cross-word co-articulation modeling, and (h) combining MFCC and PLP decoding outputs using confusion networks. We have reduced the Chinese character error rate (CER) of the BBN-2003 development test set from 53.8 % to 46.8 % after (a)+(b)+(c)+(f)+(g) are combined. Further reduction in CER is anticipated after integrating all improvements. 1.
Modeling Lexical Tones for Mandarin Large Vocabulary Continuous Speech Recognition
, 2006
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Building an ASR System for Noisy Environments: SRI's 2001 SPINE Evaluation System
- PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SPOKEN LANGUAGE PROCESSING
, 2002
"... We describe SRI's recognition system as used in the 2001 DARPA Speech in Noisy Environments (SPINE) evaluation. The SPINE task involves recognition of speech in simulated military environments. The task had some unique challenges, including segmentation of foreground speech from noisy backgroun ..."
Abstract
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We describe SRI's recognition system as used in the 2001 DARPA Speech in Noisy Environments (SPINE) evaluation. The SPINE task involves recognition of speech in simulated military environments. The task had some unique challenges, including segmentation of foreground speech from noisy background, the need for robust acoustic models to handle noisy speech, and development of language models from limited training data. In developing the SRI evaluation system for this task, we addressed each of these challenges using a combination of state-of-the-art techniques, including several types of feature normalization, model adaptation, class-based language modeling, multi-pass segmentation and recognition, and word posterior-based decoding and system combination.
GRADIENT BOOSTING LEARNING OF HIDDEN MARKOV MODELS
"... In this paper, we present a new training algorithm, gradient boosting learning, for Gaussian mixture density (GMD) based acoustic models. This algorithm is based on a function approximation scheme from the perspective of optimization in function space rather than parameter space, i.e., stage-wise ad ..."
Abstract
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In this paper, we present a new training algorithm, gradient boosting learning, for Gaussian mixture density (GMD) based acoustic models. This algorithm is based on a function approximation scheme from the perspective of optimization in function space rather than parameter space, i.e., stage-wise additive expansions of GMDs are used to search for optimal models instead of gradient descent optimization of model parameters. In the proposed approach, GMD starts from a single Gaussian and is built up by sequentially adding new components. Each new component is globally selected to produce optimal gain in the objective function. MLE and MMI are unified under the H-criterion, which is optimized by the extended BW (EBW) algorithm. A partial extended EM algorithm is developed for stage-wise optimization of new components. Experimental results on WSJ task demonstrate that the new algorithm leads to improved model quality and recognition performance. 1.
