Search results for: “stolcke”
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Neural-Network Based Measures of Confidence for Word Recognition
This paper proposes a probabilstic framework to define and evaluate confidence measures for word recognition. We describe a novel method to combine different knowledge sources and estimate the confidence in a word hypothesis, via a neural network.
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Acoustic Modeling for the SRI Hub4 Partitioned Evaluation Continuous Speech Recognition System
We describe the development of the SRI system evaluated in the 1996 DARPA continuous speech recognition (CSR) Hub4 partitioned evaluation (PE). The task for the Hub4 evaluation was to recognition speech from broadcast television and radio shows.
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Word Predictability After Hesitations: A Corpus-based Study
We ask whether lexical hesitations in spontaneous speech tend to precede words that are difficult to predict. We define predictability in terms of both transition probability and entropy, in the context of an N-gram language model.
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Automatic Linguistic Segmentation of Conversational Speech
We present a simple automatic segmenter of transcripts based on N-gram language modeling. We also study the relevance of several word-level features for segmentation performance. Using only word-level information, we achieve 85% recall and 70% precision on linguistic boundary detection.
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Modeling Pitch Range Variation Within and Across Speakers: Predicting F0 Targets when “Speaking Up”
We study F0 variation produced by “speaking up”, as part of a larger study of pitch range variation within and across speakers. We provide a function to predict target F0 values in this “raised” mode from F0 values at corresponding locations in speech produced in a neutral mode.
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Statistical Language Modeling for Speech Disfluencies
We introduce a language model that predicts disfluencies probabilistically and uses an edited, fluent context to predict following words. It uses dynamic programming to compute the probability of a word sequence, taking into account possible hidden disfluency events.
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Noise-resistant Feature Extraction and Model Training for Robust Speech Recognition
We present a novel noise-robust feature extraction algorithm that is a combination of our previously developed minimum mean square error (MMSE) log-energy estimation algorithm and the probabilistic optimum filtering (POF) algorithm.
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An Efficient Probabilistic Context-Free Parsing Algorithm that Computes Prefix Probabilities
We describe an extension of Earley’s parser for stochastic context-free grammars that computes quantities given a stochastic context-free grammar and an input string.