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We present detailed analysis of phoneme recognition performance of a context dependent tied-state triphone Gaussian Mixture Model Hidden Markov Model (CD-GMM-HMM) acoustic model (state-of-the-art large acoustic model (AM)) and a four hidden layer context dependent Deep Neural Network (CD-DNN-HMM) AM on the WSJ speech corpus. Using a bigram phoneme language model, phoneme recognition experiments are...
In this paper we investigate whether a layered architecture that has already proven its value for small tasks, works for a system with large lexica (400k words) and language models (5-grams) as well. The architecture was designed to decouple phone and word recognition which allows for the integration of more complex linguistic components, especially at the sub-word level. It was tested on the Dutch...
This paper describes a new approach to modeling duration for LVCSR using SCARF, a toolkit for speech recognition with segmental conditional random fields. We utilize SCARF's ability to integrate long-span, segment-level features to design and test duration models that help discriminate between correct and incorrect word hypotheses. We show that the duration distributions of correct and incorrect word...
We propose an improved spoken term detection approach that uses support vector machines trained with lattice context consistency. The basic idea is that the same term usually have similar context, while quite different context usually implies the terms are different. Support vector machine can be trained using query context feature vectors obtained from the lattice to estimate better scores for ranking,...
This paper presents a new framework integrating different relevance feedback scenarios (pseudo relevance feedback and user relevance feedback in short- and long-term context) and different approaches (model- and example-based) in a spoken term detection system, and shows the retrieval performance can be improved step by step. It is found that short-term context user relevance feedback can further...
We describe the design of IBM's Attila speech recognition toolkit. We show how the combination of a highly modular and efficient library of low-level C++ classes with simple interfaces, an interconnection layer implemented in a modern scripting language (Python), and a standardized collection of scripts for system-building produce a flexible and scalable toolkit that is useful both for basic research...
Word based models are widely used in speech recognition since they typically perform well. However, the question of whether it is better to use a word-based or a character-based model warrants being for the Mandarin Chinese language. Since Chinese is written without any spaces or word delimiters, a word segmentation algorithm is applied in a pre-processing step prior to training a word-based language...
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