PICES Press, Vol. 20, No. 2, Summer 2012

•The 2012 Inter-sessional Science Board Meeting: A Note from Science Board Chairman (pp. 1-4) ◾PICES Interns (p. 4) ◾2012 Inter-sessional Workshop on a Roadmap for FUTURE (pp. 5-8) ◾Second Symposium on “Effects of Climate Change on the World’s Oceans” (pp. 9-13) ◾2012 Yeosu Workshop on “Framework for Ocean Observing” (pp. 14-15) ◾2012 Yeosu Workshop on “Climate Change Projections” (pp. 16-17) ◾2012 Yeosu Workshop on “Coastal Blue Carbon” (pp. 18-20) ◾Polar Comparisons: Summary of 2012 Yeosu Workshop (pp. 21-23) ◾2012 Yeosu Workshop on “Climate Change and Range Shifts in the Oceans" (pp. 24-27) ◾2012 Yeosu Workshop on “Beyond Dispersion” (pp. 28-30) ◾2012 Yeosu Workshop on “Public Perception of Climate Change” (pp. 31, 50) ◾PICES Working Group 20: Accomplishments and Legacy (pp. 32-33) ◾The State of the Western North Pacific in the Second Half of 2011 (pp. 34-35) ◾Another Cold Winter in the Gulf of Alaska (pp. 36-37) ◾The Bering Sea: Current Status and Recent Events (pp. 38-40) ◾PICES/ICES 2012 Conference for Early Career Marine Scientists (pp. 41-43) ◾Completion of the PICES Seafood Safety Project – Indonesia (pp. 44-46) ◾Oceanography Improves Salmon Forecasts (p. 47) ◾2012 GEOHAB Open Science Meeting (p. 48-50) ◾Shin-ichi Ito awarded 2011 Uda Prize (p. 50

Linguistically Aided Speaker Diarization Using Speaker Role Information

Speaker diarization relies on the assumption that speech segments corresponding to a particular speaker are concentrated in a specific region of the speaker space; a region which represents that speaker's identity. These identities are not known a priori, so a clustering algorithm is typically employed, which is traditionally based solely on audio. Under noisy conditions, however, such an approach poses the risk of generating unreliable speaker clusters. In this work we aim to utilize linguistic information as a supplemental modality to identify the various speakers in a more robust way. We are focused on conversational scenarios where the speakers assume distinct roles and are expected to follow different linguistic patterns. This distinct linguistic variability can be exploited to help us construct the speaker identities. That way, we are able to boost the diarization performance by converting the clustering task to a classification one. The proposed method is applied in real-world dyadic psychotherapy interactions between a provider and a patient and demonstrated to show improved results.Comment: from v1: restructured Introduction and Background, added experimental results with ASR text and language-only baselin

Complex Politics: A Quantitative Semantic and Topological Analysis of UK House of Commons Debates

This study is a first, exploratory attempt to use quantitative semantics techniques and topological analysis to analyze systemic patterns arising in a complex political system. In particular, we use a rich data set covering all speeches and debates in the UK House of Commons between 1975 and 2014. By the use of dynamic topic modeling (DTM) and topological data analysis (TDA) we show that both members and parties feature specific roles within the system, consistent over time, and extract global patterns indicating levels of political cohesion. Our results provide a wide array of novel hypotheses about the complex dynamics of political systems, with valuable policy applications

Improving the Generalizability of Speech Emotion Recognition: Methods for Handling Data and Label Variability

Emotion is an essential component in our interaction with others. It transmits information that helps us interpret the content of what others say. Therefore, detecting emotion from speech is an important step towards enabling machine understanding of human behaviors and intentions. Researchers have demonstrated the potential of emotion recognition in areas such as interactive systems in smart homes and mobile devices, computer games, and computational medical assistants. However, emotion communication is variable: individuals may express emotion in a manner that is uniquely their own; different speech content and environments may shape how emotion is expressed and recorded; individuals may perceive emotional messages differently. Practically, this variability is reflected in both the audio-visual data and the labels used to create speech emotion recognition (SER) systems. SER systems must be robust and generalizable to handle the variability effectively. The focus of this dissertation is on the development of speech emotion recognition systems that handle variability in emotion communications. We break the dissertation into three parts, according to the type of variability we address: (I) in the data, (II) in the labels, and (III) in both the data and the labels. Part I: The first part of this dissertation focuses on handling variability present in data. We approximate variations in environmental properties and expression styles by corpus and gender of the speakers. We find that training on multiple corpora and controlling for the variability in gender and corpus using multi-task learning result in more generalizable models, compared to the traditional single-task models that do not take corpus and gender variability into account. Another source of variability present in the recordings used in SER is the phonetic modulation of acoustics. On the other hand, phonemes also provide information about the emotion expressed in speech content. We discover that we can make more accurate predictions of emotion by explicitly considering both roles of phonemes. Part II: The second part of this dissertation addresses variability present in emotion labels, including the differences between emotion expression and perception, and the variations in emotion perception. We discover that it is beneficial to jointly model both the perception of others and how one perceives one’s own expression, compared to focusing on either one. Further, we show that the variability in emotion perception is a modelable signal and can be captured using probability distributions that describe how groups of evaluators perceive emotional messages. Part III: The last part of this dissertation presents methods that handle variability in both data and labels. We reduce the data variability due to non-emotional factors using deep metric learning and model the variability in emotion perception using soft labels. We propose a family of loss functions and show that by pairing examples that potentially vary in expression styles and lexical content and preserving the real-valued emotional similarity between them, we develop systems that generalize better across datasets and are more robust to over-training. These works demonstrate the importance of considering data and label variability in the creation of robust and generalizable emotion recognition systems. We conclude this dissertation with the following future directions: (1) the development of real-time SER systems; (2) the personalization of general SER systems.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147639/1/didizbq_1.pd

PICES Press, Vol. 17, No. 1, January 2009

Major Outcomes from the 2008 PICES Annual Meeting: A Note from the Chairman (pdf, 0.1 Mb) PICES Science – 2008 (pdf, 0.1 Mb) 2008 PICES Awards (pdf, 0.3 Mb) Charles B. Miller – A Selective Biography (pdf, 0.4 Mb) Latest and Upcoming PICES Publications (pdf, 0.1 Mb) 2008 OECOS Workshop in Dalian (pdf, 0.2 Mb) PICES Calendar (pdf, 0.1 Mb) 2008 PICES Workshop on “Climate Scenarios for Ecosystem Modeling (II)” (pdf, 0.1 Mb) PICES/ESSAS Workshop on “Marine Ecosystem Model Inter-Comparisons” (pdf, 0.2 Mb) Highlights of the PICES Seventeenth Annual Meeting (pdf, 0.5 Mb) 2008 PICES Summer School on “Ecosystem-Based Management” (pdf, 0.3 Mb) 4th PICES Workshop on “The Okhotsk Sea and Adjacent Areas” (pdf, 0.2 Mb) PICES WG 21 Rapid Assessment Surveys (pdf, 0.4 Mb) PICES Interns (pdf, 0.3 Mb) PICES @ Oceans in a High CO2 World (pdf, 0.1 Mb) Coping with Global Change in Marine Social–Ecological Systems: An International Symposium (pdf, 0.1 Mb) The State of the Western North Pacific in the First Half of 2008 (pdf, 1.3 Mb) State of the Northeast Pacific through 2008 (pdf, 0.3 Mb) The Bering Sea: Current Status and Recent Events (pdf, 0.2 Mb) An Opinion Born of Years of Observing Timeseries Observations (pdf, 0.1 Mb) New Chairman for the PICES Fishery Science Committee (pdf, 0.1 Mb

PICES Press, Vol. 15, No. 2, July 2007

Contents [Individual sections are downloadable from the official URL link listed below]: PICES Science in 2007 (pdf, 0.1 Mb) 2007 Wooster Award (pdf, 0.1 Mb) FUTURE - A milestone reached but our task is not done (pdf, < 0.1 Mb) International symposium on "Reproductive and Recruitment Processes of Exploited Marine Fish Stocks" (pdf, 0.1 Mb) Recent results of the micronekton sampling inter-calibration experiment (pdf, 0.1 Mb) 2007 PICES workshop on "Measuring and monitoring primary productivity in the North Pacific" (pdf, 0.1 Mb) 2007 Harmful Algal Bloom Section annual workshop events (pdf, 0.1 Mb) A global approach for recovery and sustainability of marine resources in Large Marine Ecosystems (pdf, 0.3 Mb) Highlights of the PICES Sixteenth Annual Meeting (pdf, 0.4 Mb) Ocean acidification of the North Pacific Ocean (pdf, 0.3 Mb) Workshop on NE Pacific Coastal Ecosystems (2008 Call for Salmon Survival Forecasts) (pdf, 0.1 Mb) The state of the western North Pacific in the first half of 2007 (pdf, 0.4 Mb) PICES Calendar (pdf, 0.4 Mb) The Bering Sea: Current status and recent events (pdf, 0.3 Mb) PICES Interns (pdf, 0.3 Mb) Recent trends in waters of the subarctic NE Pacific (pdf, 0.3 Mb) Election results at PICES (pdf, 0.2 Mb) A new PICES award for monitoring and data management activities (pdf, < 0.1 Mb

Cepstral trajectories in linguistic units for text-independent speaker recognition

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-35292-8_3Proceedings of IberSPEECH, held in Madrid (Spain) on 2012.In this paper, the contributions of different linguistic units to the speaker recognition task are explored by means of temporal trajectories of their MFCC features. Inspired by successful work in forensic speaker identification, we extend the approach based on temporal contours of formant frequencies in linguistic units to design a fully automatic system that puts together both forensic and automatic speaker recognition worlds. The combination of MFCC features and unit-dependent trajectories provides a powerful tool to extract individualizing information. At a fine-grained level, we provide a calibrated likelihood ratio per linguistic unit under analysis (extremely useful in applications such as forensics), and at a coarse-grained level, we combine the individual contributions of the different units to obtain a highly discriminative single system. This approach has been tested with NIST SRE 2006 datasets and protocols, consisting of 9,720 trials from 219 male speakers for the 1side-1side English-only task, and development data being extracted from 367 male speakers from 1,808 conversations from NIST SRE 2004 and 2005 datasetsSupported by MEC grant PR-2010-123, MICINN project TEC09-14179, ForBayes project CCG10-UAM/TIC-5792 and Cátedra UAM-Telefónica

Production and perception of speaker-specific phonetic detail at word boundaries

Experiments show that learning about familiar voices affects speech processing in many tasks. However, most studies focus on isolated phonemes or words and do not explore which phonetic properties are learned about or retained in memory. This work investigated inter-speaker phonetic variation involving word boundaries, and its perceptual consequences. A production experiment found significant variation in the extent to which speakers used a number of acoustic properties to distinguish junctural minimal pairs e.g. 'So he diced them'—'So he'd iced them'. A perception experiment then tested intelligibility in noise of the junctural minimal pairs before and after familiarisation with a particular voice. Subjects who heard the same voice during testing as during the familiarisation period showed significantly more improvement in identification of words and syllable constituents around word boundaries than those who heard different voices. These data support the view that perceptual learning about the particular pronunciations associated with individual speakers helps listeners to identify syllabic structure and the location of word boundaries