Speech-Gaze Temporal Alignment for Automatic Word Acquisition in Multimodal Conversational Systems

Dec 20, 2012

10 likes | 108 Views

Share Presentation

Embed Code

Link

griffith

Download Presentation

Speech-Gaze Temporal Alignment for Automatic Word Acquisition in Multimodal Conversational Systems

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

Automatic Speech Recognition

Automatic Speech Recognition. Slides now available at www.informatics.manchester.ac.uk/~harold/LELA300431/. Automatic speech recognition. What is the task? What are the main difficulties? How is it approached? How good is it? How much better could it be?. What is the task?.

817 views • 34 slides

Temporal Alignment

Temporal Alignment. Motivation To accomplish sensor fusion, it is necessary for different sensors to agree on a common notion of time.Observations obtained from different sensors need to be share a consistent index of time.According to the applications and available resources, four major scena

418 views • 31 slides

Automatic Speech Recognition

Automatic Speech Recognition. The Noisy Channel Model. Automatic speech recognition (ASR) is a process by which an acoustic speech signal is converted into a set of words [Rabiner et al., 1993] The noisy channel model [Lee et al., 1996]

497 views • 16 slides

Speech and multimodal

Speech and multimodal. Jesse Cirimele. papers. “Multimodal interaction” Sharon Oviatt “Designing SpeechActs” Yankelovich et al. Why multimodal?. More transparent, flexible, efficient, and powerfully expressive means of HCI. flexiblility. Modality choice for different situations

373 views • 20 slides

Multilingual Conversational Systems

SPEECH SYNTHESIS. SPEECH SYNTHESIS. LANGUAGE GENERATION. SPEECH SYNTHESIS. Rules. Graphs & Tables. DIALOGUE MANAGER. DATABASE. Meaning Representation. DISCOURSE CONTEXT. LANGUAGE UNDERSTANDING. SPEECH RECOGNITION. Models. Models. Models. Rules. Language Independent.

618 views • 43 slides

Automatic speech recognition

Automatic speech recognition. Contents ASR systems ASR applications ASR courses Presented by Kalle Palomäki Teaching material: Kalle Palomäki & Mikko Kurimo. About Kalle. Background: Acoustics and audio, auditory brain measurements, hearing models, noise robust ASR PhD 2005 at TKK

1.04k views • 61 slides

Grounding in Conversational Systems

Grounding in Conversational Systems. Dan Bohus January 2003 Dialogs on Dialogs Reading Group Carnegie Mellon University. Overview. Early grounding theories Discourse Contributions - Clark & Schaefer Conversational acts – Traum A Computational Framework (Horvitz, Paek) Principles Systems

454 views • 28 slides

Multifaceted Conversational Systems

Multifaceted Conversational Systems. Robby Garner Robitron Software Research www.robitron.com. Albert One. Used multiple response methods. Consisted of several executables. More humanlike for having variety. Built after first two strategies failed. Based on observations of past contests.

295 views • 11 slides

Multimodal Expressive Embodied Conversational Agents

Multimodal Expressive Embodied Conversational Agents. Université Paris 8. Catherine Pelachaud. Elisabetta Bevacqua Nicolas Ech Chafai, FT Maurizio Mancini Magalie Ochs, FT Christopher Peters Radek Niewiadomski. ECAs Capabilities. Anthropomorphic autonome figures

883 views • 73 slides

Sentence Unit Detection in Conversational Dialog Speech

Sentence Unit Detection in Conversational Dialog Speech. Add graphic of sound wave file Text transcription of interspersed speaker words without . , ?. Elizabeth Lingg Tejaswi Tennetti Anand Madhavan. Sentence Units. Questions Back Channels Statements. Data Used.

196 views • 9 slides

Robust Methods for Automatic Transcription and Alignment of Speech Signals

Robust Methods for Automatic Transcription and Alignment of Speech Signals. Course presentation: Speech Recognition Leif Grönqvist (leifg@ling.gu.se) Växjö University (Mathematics and Systems Engineering) GSLT (Graduate School of Language Technology)

328 views • 14 slides

Problems of Modeling Phone Deletion in Conversational Speech for Speech Recognition

Problems of Modeling Phone Deletion in Conversational Speech for Speech Recognition. Brian Mak and Tom Ko Hong Kong University of Science and Technology. Follow-up Work of Our Previous Paper.

382 views • 19 slides

Topic Learning in Text & Conversational Speech

Topic Learning in Text & Conversational Speech. Constantinos Boulis. Introduction. Definition of Topic Learning Supervised : Learn a mapping of data to topics Unsupervised : Discover new topics Applications of Topic Learning Crucial step for information access

347 views • 17 slides

Automatic Phoneme Alignment

Automatic Phoneme Alignment. Hung-Yi Lo , Jen-Wei Kuo and Hsin-Ming Wang NGASR Summer Meeting 2011 July 12, 2011. attitude. Context. In "A good attitude is unbeatable". Phonetic Transcription. ae dx ax tcl t ux dcl. Acoustic Signal. Spectrogram.

506 views • 29 slides

MINIMUM WORD CLASSIFICATION ERROR TRAINING OF HMMS FOR AUTOMATIC SPEECH RECOGNITION

MINIMUM WORD CLASSIFICATION ERROR TRAINING OF HMMS FOR AUTOMATIC SPEECH RECOGNITION. Yueng-Tien, Lo g96470198@csie.ntnu.edu.tw Speech Lab, CSIE National Taiwan Normal University. Reference : MINIMUM WORD CLASSIFICATION ERROR TRAINING OF HMMS FOR AUTOMATIC SPEECH RECOGNITION ( ICASSP 2008)

364 views • 17 slides

Speech rate affects the word error rate of automatic speech recognition systems.

Articulation Rate: Measures, Realised Lexical Form and Phone Classification in Spontaneous and Read German Speech. word error rate. # utterances. slow. fast. -3. -2. -1. 0. 1. 2. 3. articulation rate (sd).

152 views • 1 slides

Word Alignment

Word Alignment. David Kauchak CS159 – Fall 2014. Philipp Koehn. Some slides adapted from. Kevin Knight. Dan Klein. School of Informatics University of Edinburgh. USC/Information Sciences Institute USC/Computer Science Department. Computer Science Department UC Berkeley. Admin.

792 views • 68 slides

Learning Long-Term Temporal Features for Conversational Speech Recognition

Learning Long-Term Temporal Features for Conversational Speech Recognition. A Comparative Study Barry Chen, Qifeng Zhu, & Nelson Morgan (with many thanks to Hynek Hermansky and Andreas Stolcke). Log-Critical Band Energies. Conventional Feature Extraction. Log-Critical Band Energies.

284 views • 19 slides

Automatic Speech Recognition

Automatic Speech Recognition. Julia Hirschberg CS 6998. What is speech recognition?. Transcribing words? Understanding meaning?. It’s hard to recognize speech. People speak in very different ways Across speaker variation Within speaker variation

484 views • 27 slides

— Automatic Optical Alignment for AFM

The Project on Micro Manipulation Contest. — Automatic Optical Alignment for AFM. Adviser: Li-Chen Fu. Team Members: S. K. Hung, C. F. Tsai, Y. P. Shyu, D. J. Tzou. 2003 Sep. 19. Outlines. Introduction A Novel Optical Design for AFM The Profile of This Project. 2/17. 1. Introduction.

319 views • 17 slides

Word Alignment

Word Alignment. David Kauchak CS159 – Fall 2019. Philipp Koehn. Some slides adapted from. Kevin Knight. Dan Klein. School of Informatics University of Edinburgh. USC/Information Sciences Institute USC/Computer Science Department. Computer Science Department UC Berkeley. Admin.

900 views • 88 slides

Grounding in Conversational Systems

304 views • 28 slides

More Related

Speech-Gaze Temporal Alignment for Automatic Word Acquisition in Multimodal Conversational Systems

Speech-Gaze Temporal Alignment for Automatic Word Acquisition in Multimodal Conversational Systems

Presentation Transcript

Automatic Speech Recognition

Temporal Alignment

Automatic Speech Recognition

Speech and multimodal

Multilingual Conversational Systems

Automatic speech recognition

Grounding in Conversational Systems

Multifaceted Conversational Systems

Multimodal Expressive Embodied Conversational Agents

Sentence Unit Detection in Conversational Dialog Speech

Robust Methods for Automatic Transcription and Alignment of Speech Signals

Problems of Modeling Phone Deletion in Conversational Speech for Speech Recognition

Topic Learning in Text &amp; Conversational Speech

Automatic Phoneme Alignment

MINIMUM WORD CLASSIFICATION ERROR TRAINING OF HMMS FOR AUTOMATIC SPEECH RECOGNITION

Speech rate affects the word error rate of automatic speech recognition systems.

Word Alignment

Learning Long-Term Temporal Features for Conversational Speech Recognition

Automatic Speech Recognition

— Automatic Optical Alignment for AFM

Word Alignment

Grounding in Conversational Systems

Topic Learning in Text & Conversational Speech