Prosody and Emotion 5
HomeFull List of Titles
1: ICSLP'98 Proceedings
Keynote SpeechesText-To-Speech Synthesis 1Spoken Language Models and Dialog 1Prosody and Emotion 1Hidden Markov Model Techniques 1Speaker and Language Recognition 1Multimodal Spoken Language Processing 1Isolated Word RecognitionRobust Speech Processing in Adverse Environments 1Spoken Language Models and Dialog 2Articulatory Modelling 1Talking to Infants, Pets and LoversRobust Speech Processing in Adverse Environments 2Spoken Language Models and Dialog 3Speech Coding 1Articulatory Modelling 2Prosody and Emotion 2Neural Networks, Fuzzy and Evolutionary Methods 1Utterance Verification and Word Spotting 1 / Speaker Adaptation 1Text-To-Speech Synthesis 2Spoken Language Models and Dialog 4Human Speech Perception 1Robust Speech Processing in Adverse Environments 3Speech and Hearing Disorders 1Prosody and Emotion 3Spoken Language Understanding Systems 1Signal Processing and Speech Analysis 1Spoken Language Generation and Translation 1Spoken Language Models and Dialog 5Segmentation, Labelling and Speech Corpora 1Multimodal Spoken Language Processing 2Prosody and Emotion 4Neural Networks, Fuzzy and Evolutionary Methods 2Large Vocabulary Continuous Speech Recognition 1Speaker and Language Recognition 2Signal Processing and Speech Analysis 2Prosody and Emotion 5Robust Speech Processing in Adverse Environments 4Segmentation, Labelling and Speech Corpora 2Speech Technology Applications and Human-Machine Interface 1Large Vocabulary Continuous Speech Recognition 2Text-To-Speech Synthesis 3Language Acquisition 1Acoustic Phonetics 1Speaker Adaptation 2Speech Coding 2Hidden Markov Model Techniques 2Multilingual Perception and Recognition 1Large Vocabulary Continuous Speech Recognition 3Articulatory Modelling 3Language Acquisition 2Speaker and Language Recognition 3Text-To-Speech Synthesis 4Spoken Language Understanding Systems 4Human Speech Perception 2Large Vocabulary Continuous Speech Recognition 4Spoken Language Understanding Systems 2Signal Processing and Speech Analysis 3Human Speech Perception 3Speaker Adaptation 3Spoken Language Understanding Systems 3Multimodal Spoken Language Processing 3Acoustic Phonetics 2Large Vocabulary Continuous Speech Recognition 5Speech Coding 3Language Acquisition 3 / Multilingual Perception and Recognition 2Segmentation, Labelling and Speech Corpora 3Text-To-Speech Synthesis 5Spoken Language Generation and Translation 2Human Speech Perception 4Robust Speech Processing in Adverse Environments 5Text-To-Speech Synthesis 6Speech Technology Applications and Human-Machine Interface 2Prosody and Emotion 6Hidden Markov Model Techniques 3Speech and Hearing Disorders 2 / Speech Processing for the Speech and Hearing Impaired 1Human Speech ProductionSegmentation, Labelling and Speech Corpora 4Speaker and Language Recognition 4Speech Technology Applications and Human-Machine Interface 3Utterance Verification and Word Spotting 2Large Vocabulary Continuous Speech Recognition 6Neural Networks, Fuzzy and Evolutionary Methods 3Speech Processing for the Speech-Impaired and Hearing-Impaired 2Prosody and Emotion 72: SST Student Day
SST Student Day - Poster Session 1SST Student Day - Poster Session 2Author Index
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Multimedia Files
The Tilt Intonation Model
Authors:
Paul Taylor, University of Edinburgh (U.K.)
Page (NA) Paper number 827
Abstract:
The tilt intonation model facilitates automatic analysis and synthesis of intonation. The analysis algorithm detects intonational events in F0 contours and parameterises them in terms of the continuously varying parameters. We describe the analysis system and give results for speaker independent spontaneous dialogue speech. We then describe a synthesis algorithm which can generate F0 contours given a tilt parameterisation of an utterance. We give results showing how well the automatically produced contours match natural ones. The paper concludes with a discussion of the linguistic relevance of the tilt parameters and show that this is both a useful and natural way of representing intonation.
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Analysis of Occurrence of Pauses and Their Durations in Japanese Text Reading
Authors:
Hiroya Fujisaki, Department of Applied Electronics, Science University of Tokyo (Japan)
Sumio Ohno, Department of Applied Electronics, Science University of Tokyo (Japan)
Seiji Yamada, Department of Applied Electronics, Science University of Tokyo (Japan)
Page (NA) Paper number 831
Abstract:
Pauses play important roles both for the intelligibility and the naturalness of speech. Their occurrences and durations in text reading are influenced by syntactic structures of the text as well as by physiological constraints of respiration on the part of the speaker. The present paper describes some of the preliminary findings on Japanese text reading, especially on the effects of the syntactic role of the preceding phrase on the rate of occurrence and the duration of a pause at a syntactic boundary.
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A Statistical Study of Pitch Target Points in Five Languages
Authors:
Estelle Campione, Université de Provence (France)
Jean Véronis, Université de Provence (France)
Page (NA) Paper number 845
Abstract:
We present the results of a large-scale statistical study of pitch target points in five languages, on a corpus comprising 4 hours 20 minutes of speech and involving 50 different speakers. The entire corpus has been stylized automatically by a technique reducing the F0 contour to a series of target points representing the significant pitch changes. It was then entirely verified by experts using a resynthesis method, in order to ensure that there was no audible difference with the original. The set of ca. 50000 pitch target points thus obtained was then analyzed from a statistical point of view. In this paper we describe the main results of this study, in terms of frequency distribution of target points, pitch movements and relation of pitch movements to time interval. Our study reveals interesting differences across languages and sex.
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Fully Automatic Prosody Generator For Text-to-Speech
Authors:
Fabrice Malfrère, Faculté Polytechnique de Mons (Belgium)
Thierry Dutoit, Faculté Polytechnique de Mons (Belgium)
Piet Mertens, K.U. Leuven - Département de Linguistique (Belgium)
Page (NA) Paper number 355
Abstract:
Text-to-Prosody systems based on the use of prosodic databases extracted from natural speech will be a key point for further development of new Text-to-Speech systems. This paper describes a system using such speech databases to generate the rhythm and the intonation of a French written text. The system is based on a very crude chinks 'n chunks prosodic phrasing algorithm and on a prosodic analysis of a natural speech database. The rhythm of the synthetic speech is generated with a CART tree trained on a large mono-speaker speech corpus. The acoustic aspect of the intonation is derived from a set of prosodic patterns automatically derived from the same speech corpus. The system has been tested on single sentences and news paragraphs. Informal listening tests have shown that the resulting prosody is convincing most of the time.
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0355_01.WAV(was: sound355_01.wav) | Synthesized French sentence: 'Le petit canard apprend à nager' (The little duck learns to swim). File type: Sound File Format: Sound File: WAV Tech. description: Sound file: 16000Hz, 16 bits/sample, mono, pcm,pc windows wav format Creating Application:: Unknown Creating OS: Unknown |
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Automatic Prosodic Labeling of 6 Languages
Authors:
Halewijn Vereecken, ELIS, University of Ghent (Belgium)
Jean-Pierre Martens, ELIS, University of Ghent (Belgium)
Cynthia Grover, Lernout & Hauspie Speech Products NV (Belgium)
Justin Fackrell, Lernout & Hauspie Speech Products NV (Belgium)
Bert Van Coile, Lernout & Hauspie Speech Products NV (Belgium)
Page (NA) Paper number 45
Abstract:
This contribution describes a method for the automatic prosodic labeling of multi-lingual speech data. The automatic labeler assigns a boundary strength between 0 and 3 to each word boundary, and a word prominence between 0 and 9 to each word. The speech signal and its orthographic representation are first transformed to feature vectors comprising acoustic and linguistic features such as pitch, duration, energy, part-of-speech, punctuation, word frequency and stress. Next, the feature vectors are mapped to prosodic labels via a cascade of multi-layer perceptrons. Experiments on 6 different languages demonstrate that combining acoustic with linguistic features yields a better performance than obtainable on the basis of acoustic features alone. We also present experiments in which we assess the influence of the quality of the underlying phonetic segmentation and labeling on the prosodic labeling performance.
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Automatic Utterance Type Detection Using Suprasegmental Features
Authors:
Helen Wright, Centre for Speech Technology Research, University of Edinburgh (U.K.)
Page (NA) Paper number 575
Abstract:
The goal of the work presented here is to automatically predict the type of an utterance in spoken dialogue by using automatically extracted suprasegmental information. For this task we present and compare three stochastic algorithms: hidden Markov models, artificial neural nets, and classification and regression trees. These models are easily trainable, reasonably robust and fit into the probabilistic framework required for speech recognition. Utterance type detection is dependent on the assumption that different types of utterances have different suprasegmental characteristics. The categorisation of these utterance types is based on the theory of conversation games and consists of 12 move types (e.g. reply to a question, wh-question, acknowledgement). The system is speaker independent and is trained on spontaneous goal-directed dialogue collected from Canadian speakers. This utterance type detector is used in an automatic speech recognition system to reduce word error rate.