Author: Kristin Precoda
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Classification of Lexical Stress Using Spectral and Prosodic Features for Computer-assisted Language Learning Systems
We present a system for detection of lexical stress in English words spoken by English learners. This system was designed to be part of the EduSpeak® computer-assisted language learning (CALL) software.
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Lexical Stress Classification for Language Learning Using Spectral and Segmental Features
We present a system for detecting lexical stress in English words spoken by English learners. The system uses both spectral and segmental features to detect three levels of stress for each syllable in a word.
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Detecting Leadership and Cohesion in Spoken Interactions
We present a system for detecting leadership and group cohesion in multiparty dialogs and broadcast conversations in English and Mandarin.
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Unsupervised topic modeling for leader detection in spoken discourse
In this paper, we describe a method for leader detection in multiparty spoken discourse that relies on unsupervised topic modeling to segment the discourse automatically.
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Detection of agreement and disagreement in broadcast conversations
We present Conditional Random Fields based approaches for detecting agreement/disagreement between speakers in English broadcast conversation shows.
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Automatic identification of speaker role and agreement/disagreement in broadcast conversation
We present supervised approaches for detecting speaker roles and agreement/disagreement between speakers in broadcast conversation shows in three languages: English, Arabic, and Mandarin.
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Implementing SRI’s Pashto speech-to-speech translation system on a smartphone
We describe our recent effort implementing SRI’s UMPC-based Pashto speech-to-speech (S2S) translation system on a smart phone running the Android operating system.
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EduSpeak®: A Speech Recognition and Pronunciation Scoring Toolkit for Computer-Aided Language Learning Applications
SRI International’s EduSpeak® system is a SDK that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology.
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Recent advances in SRI’s IraqComm Iraqi Arabic-English speech-to-speech translation system
We summarize recent progress on SRI’s IraqComm™ IraqiArabic-English two-way speech-to-speech translation system.
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IraqComm: A Next Generation Translation System
This paper describes the IraqComm translation system that mediates and translates spontaneous conversations between an English speaker and a speaker of colloquial Iraqi Arabic.
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Speech Translation for Low-Resource Languages: The Case of Pashto
We present a number of challenges and solutions that have arisen in the development of a speech translation system for American English and Pashto, highlighting those specific to a very low resource language.
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A Fine-Grained Evaluation Method for Speech-to-Speech Machine Translation Using Concept Annotations
This paper describes the development of a concept annotation method for evaluating a narrow domain speech-to-speech translation system and discusses how the scores produced by that method relate to naïve human judgements about the quality of translations.