Andreas Stolcke

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SRILM is a collection of C++ libraries, executable programs, and helper scripts designed to allow both production of and experimentation with statistical language models for speech recognition and other applications. SRILM is freely available for noncommercial purposes. The toolkit supports creation and evaluation of a variety of language model types based(More)
We describe a new framework for distilling information from word lattices to improve the accuracy of speech recognition and obtain a more perspicuous representation of a set of alternative hypotheses. In the standard MAP decoding approach the recognizer outputs the string of words corresponding to the path with the highest posterior probability given the(More)
We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speechact-like units such as STATEMENT,QUESTION, BACKCHANNEL,AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence.(More)
This paper extends the within-class covariance normalization (WCCN) technique described in [1, 2] for training generalized linear kernels. We describe a practical procedure for applying WCCN to an SVM-based speaker recognition system where the input feature vectors reside in a high-dimensional space. Our approach involves using principal component analysis(More)
We have collected a corpus of data from natural meetings that occurred at the International Computer Science Institute (ICSI) in Berkeley, California over the last three years. The corpus contains audio recorded simultaneously from head-worn and table-top microphones, word-level transcripts of meetings, and various metadata on participants, meetings, and(More)
A crucial step in processing speech audio data for informationextraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody(More)
We describe an extension of Earley's parser for stochastic context-free grammars that computes the following quantities given a stochastic context-free grammar and an input string: a) probabilities of successive prefixes being generated by the grammar; b) probabilities of substrings being generated by the nonterminals, including the entire string being(More)
We investigate the use of prosody for the detection of frustration and annoyance in natural human-computer dialog. In addition to prosodic features, we examine the contribution of language model information and speaking “style”. Results show that a prosodic model can predict whether an utterance is neutral versus “annoyed or frustrated” with an accuracy on(More)
The general topic of this thesis is the probabilistic modeling of language, in particular natural language. In probabilistic language modeling, one characterizes the strings of phonemes, words, etc. of a certain domain in terms of a probability distribution over all possible strings within the domain. Probabilistic language modeling has been applied to a(More)
Effective human and automatic processing of speech requires recovery of more than just the words. It also involves recovering phenomena such as sentence boundaries, filler words, and disfluencies, referred to as structural metadata. We describe a metadata detection system that combines information from different types of textual knowledge sources with(More)