In lexicalized phrase-structure or dependency parses, a word's modifiers tend to fall near it in the string. We show that a crude way to use dependency length as a parsing feature can substantially improve parsing...
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In this paper, we explore the use of Random Forests (RFs) in the structured language model (SLM), which uses rich syntactic information in predicting the next word based on words already seen. The goal in this work is...
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ISBN:
(纸本)0262195348
In this paper, we explore the use of Random Forests (RFs) in the structured language model (SLM), which uses rich syntactic information in predicting the next word based on words already seen. The goal in this work is to construct RFs by randomly growing Decision Trees (DTs) using syntactic information and investigate the performance of the SLM modeled by the RFs in automatic speech recognition. RFs, which were originally developed as classifiers, are a combination of decision tree classifiers. Each tree is grown based on random training data sampled independently and with the same distribution for all trees in the forest, and a random selection of possible questions at each node of the decision tree. Our approach extends the original idea of RFs to deal with the data sparseness problem encountered in language modeling. RFs have been studied in the context of n-gram language modeling and have been shown to generalize well to unseen data. We show in this paper that RFs using syntactic information can also achieve better performance in both perplexity (PPL) and word error rate (WER) in a large vocabulary speech recognition system, compared to a baseline that uses Kneser-Ney smoothing.
This paper proposes a variation of synchronous grammar based on the formalism of context-free grammar by generalizing the first component of productions that models the source text, named Constraint-based Synchronous ...
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For most English words, dictionaries give various senses: e.g., "bank" can stand for a financial institution, shore, set, etc. Automatic selection of the sense intended in a given text has crucial importance...
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Exploiting unannotated natural language data is hard largely because unsupervised parameter estimation is hard. We describe deterministic annealing (Rose et al., 1990) as an appealing alternative to the Expectation-Ma...
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We describe how simple, commonly understood statistical models, such as statistical dependency parsers, probabilistic context-free grammars, and word-to-word translation models, can be effectively combined into a unif...
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We propose a novel method for inducing monolingual semantic hierarchies and sense clusters from numerous foreign-language-to-English bilingual dictionaries. The method exploits patterns of non-transitivity in translat...
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We previously proposed [1] an iterative word-selective training method to cost-effectively utilize data preparation resources without compromising system performance. We continue this work and investigate the robustne...
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We previously proposed [1] an iterative word-selective training method to cost-effectively utilize data preparation resources without compromising system performance. We continue this work and investigate the robustness of our active learning approach with respect to the starting conditions and further propose a stopping criterion that supports our objective to make effective use of transcription effort while minimizing system error. In particular, we demonstrate robustness to seven initial conditions, showing that we can select around 20 hours of training data and achieve a range of error rates between 8.6% and 9.0%, compared to an error rate of 10% when using all 50 hours of the training set. Additionally, we give empirical evidence that our proposed stopping criterion is in general a good predictor of when the minimum error rate is achieved, demonstrated for each of the initial conditions.
While a number of studies have investigated various speech enhancement and noise suppression schemes, most consider either a single channel or array processing framework. Clearly there are potential advantages in leve...
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While a number of studies have investigated various speech enhancement and noise suppression schemes, most consider either a single channel or array processing framework. Clearly there are potential advantages in leveraging the strengths of array processing solutions in suppressing noise from a direction other than the speaker, with that seen in single channel methods that include speech spectral constraints or psychoacoustically motivated processing. In this paper, we propose to integrate a combined fixed/adaptive beamforming algorithm (CFA-BF) for speech enhancement with two single channel methods based on speech spectral constrained iterative processing (Auto-LSP), and an auditory masked threshold based method using equivalent rectangular bandwidth filtering (GMMSE-AMT-ERB). After formulating the method, we evaluate performance on a subset of the TIMIT corpus with four real noise sources. We demonstrate a consistent level of noise suppression and voice communication quality improvement using the proposed method as reflected by an overall average 26dB increase in SegSNR from the original degraded audio corpus.
Noisy cars are very difficult listening environments for persons with hearing loss. While there have been numerous studies in the field of speech enhancement for car noise environments, the majority of these studies h...
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Noisy cars are very difficult listening environments for persons with hearing loss. While there have been numerous studies in the field of speech enhancement for car noise environments, the majority of these studies have focused on noise reduction for normal hearing individuals. In this paper, we present recent results in the development of more effective speech capture and enhancement processing for wireless voice interaction for persons with hearing loss in real car environments. We first present a data collection experiment for a proposed FM wireless transmission scenario using a 5-channel microphone array in the car, followed by several alternative speech enhancement algorithms. After formulating 6 different processing methods, we evaluate the performance by SegSNR improvement using data recorded in a moving car environment. Among the 6 processing configurations, the combined fixed/adaptive beamforming (CFA-BF) obtains the highest level of SegSNR improvement by up to 2.65 dB.
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