An Analysis of Sentence Segmentation Features For

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An Analysis of Sentence Segmentation Features for
Broadcast News, Broadcast Conversations, and Meetings

Sebastien Cuendet cuendet@icsi.berkeley.edu
Elizabeth Shriberg ees@speech.sri.com
Benoit Favre favre@icsi.berkeley.edu

James Fung jgf@icsi.berkeley.edu
Dilek Hakkani-Tur dilek@icsi.berkeley.edu

ICSI
SRI International

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Menlo Park, CA 94025, USA
1947 Center Street
Berkeley, CA 94704, USA

ABSTRACT General Terms Keywords

1. INTRODUCTION

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2. METHOD 2.1 Data and annotations

2.2 Automatic speech recognition

2.3 Features
2.5 Metrics

Lexical features.

2.6 Chance performance computation

Prosodic Features.

3. RESULTS AND DISCUSSION 3.1 Performance by feature group
2.4 Boosting Classiﬁers

TDT4 ref TDT4 stt MRDA ref MRDA stt BC stt

80 60 40 20
0

Chance
Dur
Energy Pitch
Turn
Pause AllPros
Lex Lex+Pau ALL

Features

3.2 Performance by feature subgroup

0.40 0.35 0.30 0.25 0.20

TDT4 stt MRDA stt BC stt

PITCH
SUBGROUPS

ENERGY SUBGROUPS

Range
Reset
Slope
Range
Reset
Slope

5. ACKNOWLEDGMENTS
4. SUMMARY AND CONCLUSIONS

6. REFERENCES

Recommended publications

A Bayesian Framework for Word Segmentation: Exploring the Effects of Context

Cognition 112 (2009) 21–54 Contents lists available at ScienceDirect Cognition journal homepage: www.elsevier.com/locate/COGNIT A Bayesian framework for word segmentation: Exploring the effects of context Sharon Goldwater a,*, Thomas L. Grifﬁths b, Mark Johnson c a School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh, EH8 9AB, UK b Department of Psychology, University of California, Berkeley, CA, United States c Department of Cognitive and Linguistic Sciences, Brown University, United States article info abstract Article history: Since the experiments of Saffran et al. [Saffran, J., Aslin, R., & Newport, E. (1996). Statistical Received 30 May 2008 learning in 8-month-old infants. Science, 274, 1926–1928], there has been a great deal of Revised 11 March 2009 interest in the question of how statistical regularities in the speech stream might be used Accepted 13 March 2009 by infants to begin to identify individual words. In this work, we use computational mod- eling to explore the effects of different assumptions the learner might make regarding the nature of words – in particular, how these assumptions affect the kinds of words that are Keywords: segmented from a corpus of transcribed child-directed speech. We develop several models Computational modeling within a Bayesian ideal observer framework, and use them to examine the consequences of Bayesian Language acquisition assuming either that words are independent units, or units that help to predict other units. Word segmentation We show through empirical and theoretical results that the assumption of independence causes the learner to undersegment the corpus, with many two- and three-word sequences (e.g.
The Induction of Phonotactics for Speech Segmentation

The Induction of Phonotactics for Speech Segmentation Converging evidence from computational and human learners Published by LOT phone: +31 30 253 6006 Trans 10 fax: +31 30 253 6406 3512 JK Utrecht e-mail: lot@uu.nl The Netherlands http://www.lotschool.nl Cover illustration: Elbertus Majoor, Landschap-fantasie II (detail), 1958, gouache. ISBN: 978-94-6093-049-2 NUR 616 Copyright c 2011: Frans Adriaans. All rights reserved. The Induction of Phonotactics for Speech Segmentation Converging evidence from computational and human learners Het Induceren van Fonotactiek voor Spraaksegmentatie Convergerende evidentie uit computationele en menselijke leerders (met een samenvatting in het Nederlands) Proefschrift ter verkrijging van de graad van doctor aan de Universiteit Utrecht op gezag van de rector magniﬁcus, prof.dr. J.C. Stoof, ingevolge het besluit van het college voor promoties in het openbaar te verdedigen op vrijdag 25 februari 2011 des middags te 2.30 uur door Frans Willem Adriaans geboren op 14 maart 1981 te Ooststellingwerf Promotor: Prof. dr. R.W.J. Kager To my parents, Ank & Piet CONTENTS acknowledgements xi 1 introduction 1 1.1 The speech segmentation problem ................. 1 1.2 Segmentation cues and their acquisition .............. 3 1.2.1 Theroleofsegmentationcuesinspokenwordrecognition 4 1.2.2 The acquisition of segmentation cues by infants ..... 7 1.3 The induction of phonotactics for speech segmentation ..... 11 1.3.1 Bottom-up versus top-down ................ 11 1.3.2 Computational modeling of phonotactic learning .... 12 1.3.3 Two hypotheses regarding the acquisition of phonotac- tics by infants ......................... 16 1.4 Learning mechanisms in early language acquisition ....... 17 1.4.1 Statistical learning .....................
Automatic Speech Segmentation

Alaa Ehab Sakran et al, International Journal of Computer Science and Mobile Computing, Vol.6 Issue.4, April- 2017, pg. 308-315 Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology ISSN 2320–088X IMPACT FACTOR: 6.017 IJCSMC, Vol. 6, Issue. 4, April 2017, pg.308 – 315 A Review: Automatic Speech Segmentation Alaa Ehab Sakran 1, Sherif Mahdy Abdou 23, Salah Eldeen Hamid 4, Mohsen Rashwan 25 1 PhD. Program in Computer Science, Sudan University of Science and Technology, Khartoum, Sudan 2 Research & Development International (RDI®), Giza, Egypt 3 Department of IT, Faculty of Computers and Information, Cairo University, Giza, Egypt 4 Department of Engineering and Applied Sciences, Umm Al-Qura University, Mecca, Saudi Arabia 5 Department of Electronics and Communication Engineering, Cairo University, Giza, Egypt alaa238.ehab@gmail.com, salahamed@gmail.com, {sabdou, mrashwan} @rdi-eg.com I. INTRODUCTION Automated segmentation of speech signals has been under research for over 30 years. Many speech processing systems require segmentation of Speech waveform into principal acoustic units. Segmentation is a process of breaking down a speech signal into smaller units. Segmentation is the very primary step in any voiced activated systems like speech recognition systems and training of speech synthesis systems. Speech segmentation is performed utilizing Wavelet, Fuzzy methods, Artificial Neural Networks and Hidden Markov Model. [1] [2] In this research a review of basics of speech segmentation problem and state-of-the art solutions will be investigated. In next section main characteristics of speech signal will be discussed.
A Semi-Markov Model for Speech Segmentation with an Utterance-Break Prior

A semi-Markov model for speech segmentation with an utterance-break prior Mark Sinclair1, Peter Bell1, Alexandra Birch2, Fergus McInnes1 1Centre for Speech Technology Research, 2Statistical Machine Translation Group School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK fmark.sinclair, a.birch, peter.bell, fergus.mcinnesg@ed.ac.uk Abstract Previous work on detecting segmentation of sentence like Speech segmentation is the problem of finding the end points of units has looked at using features such as prosody [10], lan- a speech utterance for passing to an automatic speech recogni- guage model scores [11] [12], translation model scores [13] and tion (ASR) system. The quality of this segmentation can have syntactic constituents [14]. [15] presents a review of some of a large impact on the accuracy of the ASR system; in this pa- this work and also motivates tuning the segmentation of speech per we demonstrate that it can have an even larger impact on to the task at hand as we do in this paper. Our approach of mod- downstream natural language processing tasks – in this case, elling global sentence length distribution is orthogonal to much machine translation. We develop a novel semi-Markov model of this previous work, and combining these information sources which allows the segmentation of audio streams into speech ut- would be beneficial. There has been some previous work which terances which are optimised for the desired distribution of sen- attempts to exploit some of these cues [16]. However, in the tence lengths for the target domain. We compare this with exist- present paper, we have limited our focus to the use of statis- ing state-of-the-art methods and show that it is able to achieve tics of utterance durations and present a novel way of exploit- not only improved ASR performance, but also to yield signifi- ing this to select a globally optimal sequence from acoustically cant benefits to a speech translation task.
Seminar, Tue 4:15Pm

On an exceptionally hot evening early in July a young man came out of the garret in which he lodged in S. Place and walked slowly, as though in hesitation, towards K. bridge. He had successfully avoided meeting his landlady on the staircase. His garret was under the roof of a high, ﬁve-storied house and was more like a cupboard than a room. The landlady who provided him with garret, dinners, and attendance, lived on the ﬂoor below, and every time he went out he was obliged to pass her kitchen, the door of which invariably stood open. And each time he passed, the young man had a sick, frightened feeling, which made him scowl and feel ashamed. He was hopelessly in debt to his landlady, and was afraid of meeting her. This was not because he was cowardly and abject, quite the contrary; but for some time past he had been in an overstrained irritable condition, verging on hypochondria. He had become so completely absorbed in himself, and isolated from his fellows that he dreaded meeting, not only his landlady, but anyone at all. He was crushed by poverty, but the anxieties of his position had ofNatural late ceased to weigh upon him.language He had given up attending to matters of practical importance; he had lost all desire to do so. Nothing that any landlady could do had a real terror for him. But to be stopped on the stairs, to be forced to listen to her trivial, irrelevant gossip, to pestering demands for payment, threats and complaints, and to rack his brains for excuses, to prevaricate, to lie—no, rather than that, he would creep down the stairs like a cat andprocessing slip out unseen.
The Polysemy of the Words That Children Learn Over Time Arxiv

The Polysemy of the Words that Children Learn over Time Bernardino Casas bcasas@cs.upc.edu 1 Neus Català ncatala@cs.upc.edu 2 Ramon Ferrer-i-Cancho rferrericancho@cs.upc.edu 1 Antoni Hernández-Fernández antonio.hernandez@upc.edu 1,3 Jaume Baixeries jbaixer@cs.upc.edu 1 1. Complexity & Quantitative Linguistics Lab, Departament de Ciències de la Computació, Laboratory for Relational Algorithmics, Complexity and Learning (LARCA), Universitat Politècnica de Catalunya, Barcelona, Catalonia. 2. Complexity & Quantitative Linguistics Lab, Departament de Ciències de la Computació, Center for Language and Speech Technologies and Applications (TALP Research Center), Universitat Politècnica de Catalunya, Barcelona, Catalonia. 3. Institut de Ciències de l0Educació, Universitat Politècnica de Catalunya, Barcelona, Catalonia. arXiv:1611.08807v2 [cs.CL] 26 Mar 2019 1 Abstract Here we study polysemy as a potential learning bias in vocabulary learning in children. Words of low polysemy could be preferred as they reduce the disambiguation effort for the listener. However, such preference could be a side-effect of another bias: the preference of chil- dren for nouns in combination with the lower polysemy of nouns with respect to other part-of-speech categories. Our results show that mean polysemy in children increases over time in two phases, i.e. a fast growth till the 31st month followed by a slower tendency towards adult speech. In contrast, this evolution is not found in adults interacting with children. This suggests that children have a preference for non-polysemous words in their early stages of vocabulary acquisition. Interestingly, the evolutionary pat- tern described above weakens when controlling for syntactic category (noun, verb, adjective or adverb) but it does not disappear completely, suggesting that it could result from a combination of a standalone bias for low polysemy and a preference for nouns.
AUTOMATIC LINGUISTIC SEGMENTATION of CONVERSATIONAL SPEECH Andreas Stolcke Elizabeth Shriberg

AUTOMATIC LINGUISTIC SEGMENTATION OF CONVERSATIONAL SPEECH Andreas Stolcke Elizabeth Shriberg Speech Technology and Research Laboratory SRI International, Menlo Park, CA 94025 stolcke@speech.sri.com ees@speech.sri.com ABSTRACT could use the knowledge incorporated in an automatic segmenter to help end-point a user’s speech input. A speech indexing and re- As speech recognition moves toward more unconstrained domains trieval system (such as for transcribed broadcast audio) could pro- such as conversational speech, we encounter a need to be able to cess its data in more meaningful units if the locations of linguistic segment (or resegment) waveforms and recognizer output into lin- segment boundaries were known. guistically meaningful units, such a sentences. Toward this end, we present a simple automatic segmenter of transcripts based on Our main motivation for the work reported here comes from speech N-gram language modeling. We also study the relevance of sev- language modeling. Experiments at the 1995 Johns Hopkins Lan- eral word-level features for segmentation performance. Using only guage Modeling Workshop showed that the quality of a language word-level information, we achieve 85% recall and 70% precision model (LM) can be improved if both training and test data are seg- on linguistic boundary detection. mented linguistically, rather than acoustically [8]. We showed in [10] and [9] that proper modeling of filled pauses requires knowl- 1. INTRODUCTION edge of linguistic segment boundaries. We found for example that Today’s large-vocabulary speech recognizers typically prefer to pro- segment-internal filled pauses condition the following words quite cess a few tens of seconds of speech at a time, to keep the time and differently from segment-initial filled pauses.
Segmentation and Morphology

Segmentation and morphology John A. Goldsmith1 Departments of Linguistics and Computer Science, The University of Chicago goldsmith@uchicago.edu A draft chapter for the Blackwell Computational Linguistics and Natural Lan- guage Processing Handbook, edited by Alex Clark, Chris Fox and Shalom Lappin. This draft formatted on 2nd May 2009. Page: 1 job: goldsmith macro: handbook.cls date/time: 2-May-2009/17:28 Page: 2 job: goldsmith macro: handbook.cls date/time: 2-May-2009/17:28 Contents Segmentation and morphology John A. Goldsmith ............................................... 1 1 Introduction................................................. 4 1.1 Generalremarks......................................... 4 1.2 Morphology............................................. 6 1.3 Staticanddynamicmetaphors ............................ 9 2 Unsupervisedlearningofwords ................................ 11 2.1 Thetwoproblemsofwordsegmentation.................... 11 2.2 Trawlingforchunks...................................... 14 2.3 Word Boundary detectors . 21 2.4 Successesandfailuresinwordsegmentation................. 22 3 Unsupervisedlearningofmorphology............................ 23 3.1 ZelligHarris ............................................ 23 3.2 Usingdescriptionlength.................................. 24 3.3 Workintheﬁeld ........................................ 27 4 Implementingcomputationalmorphologies....................... 29 4.1 Finitestatetransducers................................... 30 4.2 Morphophonology.......................................
Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm Matthew Im Ller Adam Miller Iowa State University

Iowa State University Capstones, Theses and Graduate Theses and Dissertations Dissertations 2009 Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm Matthew iM ller Adam Miller Iowa State University Follow this and additional works at: https://lib.dr.iastate.edu/etd Part of the Computer Sciences Commons Recommended Citation Miller, Matthew Miller Adam, "Unsupervised Segmentation of Audio Speech Using the Voting Experts Algorithm" (2009). Graduate Theses and Dissertations. 10725. https://lib.dr.iastate.edu/etd/10725 This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact digirep@iastate.edu. Unsupervised segmentation of audio speech using the Voting Experts algorithm by Matthew Adam Miller A thesis submitted to the graduate faculty in partial ful¯llment of the requirements for the degree of MASTER OF SCIENCE Major: Computer Science Program of Study Committee: Alexander Stoytchev, Major Professor Giora Slutzki Pavan Aduri Iowa State University Ames, Iowa 2009 Copyright °c Matthew Adam Miller, 2009. All rights reserved. ii DEDICATION To Cat. iii TABLE OF CONTENTS LIST OF TABLES . vi LIST OF FIGURES . viii ACKNOWLEDGEMENTS . xi ABSTRACT . xii CHAPTER 1. INTRODUCTION . 1 1.1 Research Questions . 4 CHAPTER 2. REVIEW OF LITERATURE . 6 2.1 Psychology of Speech Segmentation . 7 2.1.1 Statistical Learning . 8 2.1.2 Isolated Words . 10 2.1.3 Allophonic Variation . 11 2.1.4 Metrical Cues .
The Universal Dependencies Treebank of Spoken Slovenian

The Universal Dependencies Treebank of Spoken Slovenian Kaja Dobrovoljc1, Joakim Nivre2 1Institute for Applied Slovene Studies Trojina, Ljubljana, Slovenia 1Department of Slovenian Studies, Faculty of Arts, University of Ljubljana 2Department of Linguistics and Philology, Uppsala University kaja.dobrovoljc@trojina.si, joakim.nivre@lingfil.uu.se Abstract This paper presents the construction of an open-source dependency treebank of spoken Slovenian, the first syntactically annotated collection of spontaneous speech in Slovenian. The treebank has been manually annotated using the Universal Dependencies annotation scheme, a one-layer syntactic annotation scheme with a high degree of cross-modality, cross-framework and cross-language interoper- ability. In this original application of the scheme to spoken language transcripts, we address a wide spectrum of syntactic particularities in speech, either by extending the scope of application of existing universal labels or by proposing new speech-specific extensions. The initial analysis of the resulting treebank and its comparison with the written Slovenian UD treebank confirms significant syntactic differences between the two language modalities, with spoken data consisting of shorter and more elliptic sentences, less and simpler nominal phrases, and more relations marking disfluencies, interaction, deixis and modality. Keywords: dependency treebank, spontaneous speech, Universal Dependencies 1. Introduction actually out-performs state-of-the-art pipeline approaches (Rasooli and Tetreault, 2013; Honnibal and Johnson, 2014). It is nowadays a well-established fact that data-driven pars- Such heterogeneity of spoken language annotation schemes ing systems used in different speech-processing applica- inevitably leads to a restricted usage of existing spoken tions benefit from learning on annotated spoken data, rather language treebanks in linguistic research and parsing sys- than using models built on written language observation.
Sentence Boundary Detection Based on Parallel Lexical and Acoustic Models

INTERSPEECH 2016 September 8–12, 2016, San Francisco, USA Sentence Boundary Detection Based on Parallel Lexical and Acoustic Models Xiaoyin Che, Sheng Luo, Haojin Yang, Christoph Meinel Hasso Plattner Institute, University of Potsdam Prof.-Dr.-Helmert-Str. 2-3, 14482, Potsdam, Germany fxiaoyin.che, sheng.luo, haojin.yang, christoph.meinelg@hpi.de Abstract models on ASR-available TED Talks and run experiments about In this paper we propose a solution that detects sentence bound- our 2-stage joint decision scheme with different combination of ary from speech transcript. First we train a pure lexical model models. In the end comes the conclusion. with deep neural network, which takes word vectors as the only input feature. Then a simple acoustic model is also prepared. 2. Multi-Modal Structure Analysis Because the models work independently, they can be trained with different data. In next step, the posterior probabilities of The structures of multi-modal solutions can be different and both lexical and acoustic models will be involved in a heuristic have been discussed before [12]. Some researchers propose a 2-stage joint decision scheme to classify the sentence boundary single hybrid model, which takes all possible features, no matter positions. This approach ensures that the models can be up- lexical or acoustic, together as the model input [13, 14, 15, 16], dated or switched freely in actual use. Evaluation on TED Talks as shown in Figure 1-a. Some others apply a structure of shows that the proposed lexical model can achieve good results: sequential models, in which the output of model A is fed 75.5% accuracy on error-involved ASR transcripts and 82.4% into model B, as shown in Figure 1-b, where model A ac- on error-free manual references.
Speech Segmentation and Its Impact on Spoken Document Processing

SIGNAL PROCESSING MAGAZINE – MANUSCRIPT 1 Speech Segmentation and its Impact on Spoken Document Processing M. Ostendorf, B. Favre, R. Grishman, D. Hakkani-Tur, M. Harper, D. Hillard, J. Hirschberg, H. Ji, J. G. Kahn, Y. Liu, S. Maskey, E. Matusov, H. Ney, A. Rosenberg, E. Shriberg, W. Wang and C. Wooters Abstract Progress in both speech and language processing has spurred efforts to support applications that rely on spoken—rather than written—language input. A key challenge in moving from text-based documents to such “spoken documents” is that spoken language lacks explicit punctuation and formatting, which can be crucial for good performance. This paper describes different levels of speech segmentation, approaches to automatically recovering segment boundary locations, and experimental results demonstrating impact on several language processing tasks. The results also show a need for optimizing segmentation for the end task rather than independently. I. INTRODUCTION Dramatic improvements in automatic speech recognition (ASR) technology make it now possible explore how language processing techniques designed for text can be applied to spoken language. Ever increasing collections of information are available as speech recordings, including news broadcasts, meetings, debates, lectures, hearings, oral histories, and webcasts, among other types of human-directed (vs. computer-directed) communications. Given the vast amount of audio data, and the time involved in human listening, clearly some automatic means for data processing is necessary. ASR can automatically Manuscript received October 2007; revised December 2007. This work was supported by DARPA, contract No. HR0011- 06-C-0023. Distribution is unlimited. The views herein are those of the authors and do not reﬂect the views of the funding agency.