Journal ArticleDOI
Automatic discovery of topics and acoustic morphemes from speech
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TLDR
The proposed algorithm builds a lexicon enriched with topic information in three steps: transcription of an audio stream into phone sequences with a speaker- and task-independent phone recogniser, automatic lexical acquisition based on approximate string matching, and hierarchical topic clustering of the lexical entries based on a knowledge-poor co-occurrence approach.About:
This article is published in Computer Speech & Language.The article was published on 2009-04-01. It has received 11 citations till now. The article focuses on the topics: Lexical item & Semantic lexicon.read more
Citations
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Journal ArticleDOI
Command-based voice teleoperation of a mobile robot via a human-robot interface
TL;DR: In this article, a human-robot interface (HRI) is presented to teleoperate a robotic platform via the user's voice, where a speech recognition system is necessary.
Proceedings ArticleDOI
Topic modeling for spoken documents using only phonetic information
TL;DR: Recent improvements in topic modeling using only phonetic information are presented and new results using recently developed techniques for discriminative training for topic identification used in conjunction with recent improvements in SOU learning are presented.
Journal ArticleDOI
Constructing a Language From Scratch: Combining Bottom–Up and Top–Down Learning Processes in a Computational Model of Language Acquisition
TL;DR: A computational model of language acquisition is presented that is based on bootstrapping mechanisms and is usage-based in that it relies on discovered regularities to segment speech into word-like units and shows that top–down processing increases both understanding performance and segmentation accuracy.
Proceedings ArticleDOI
Learning a semantic parser from spoken utterances
Judith Gaspers,Philipp Cimiano +1 more
TL;DR: This paper presents an approach that learns a semantic parser in the form of a lexicon and an inventory of syntactic patterns from ambiguous training data which is applicable to spoken utterances and can be successfully applied to both spoken and written data.
Dissertation
A computational model for unsupervised childlike speech acquisition
TL;DR: A model for early infant speech structure acquisition implemented as layered architecture comprising phones, syllables and words and an integrated model for speech structure and imitation learning through interaction, that enables the authors' robot to learn to speak with an own voice.
References
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Journal ArticleDOI
Latent dirichlet allocation
TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.
Proceedings Article
Latent Dirichlet Allocation
TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).
Journal ArticleDOI
The Kappa Statistic in Reliability Studies: Use, Interpretation, and Sample Size Requirements
Julius Sim,Chris Wright +1 more
TL;DR: The issue of statistical testing of kappa is considered, including the use of confidence intervals, and appropriate sample sizes for reliability studies using kappa are tabulated.
Book
The generative lexicon
TL;DR: It is argued that lexical decomposition is possible if it is performed generatively and a theory of lexical inheritance is outlined, which provides the necessary principles of global organization for the lexicon, enabling us to fully integrate the authors' natural language lexicon into a conceptual whole.