49 research outputs found
Book Review: Facing the Victorious Turks: How the French Misread the Turkish War of Independence
No full textAuthor: Andrew Orr
Reviewed by: Dr. Michael S. Neiberg, chair of war studies, US Army War College
After World War I, French officials viewed the Middle East through a lens of Orientalism and imperial anxiety, leading them to misinterpret the rise of Turkish nationalism. In this gripping study, Andrew Orr reveals how flawed intelligence and racial biases shaped France’s response to Mustafa Kemal’s revolutionary movement. Facing the Victorious Turks offers a compelling reexamination of colonial misjudgment and its impact on the birth of modern Türkiye.https://press.armywarcollege.edu/parameters_bookshelf/1114/thumbnail.jp
Prosodic Characterization and Automatic Classification of Conversational Grunts in Swedish
No full textConversation is the most common use of speech. Any automatic dialog system, pretending to mimic a human, must be able to successfully detect typical sounds and meanings of spontaneous conversational speech. Automatic transcription of the function of linguistic units, sometimes refereed to as Dialog Acts (DAs), Cue Phrases or Discourse Markers is an emerging area of research. This can be done on a pure lexical level, or by using prosody alone (Laskowski and Shriberg, 2010; Goto et al., 1999), or a combination of thereof (Sridhar et al., 2009; Gravano et al., 2007). However, it is not straightforward to train a language model for non-verbal content (e.g. “mm”, “mhm”, “eh”, “em”), not only since it is questionable if these sounds are words, but also because of lack of standardized annotation schemes. Ward (2000) refer to these tokens as conversational grunts, which is also the scope of this study. Feedback tokens are usually sub-divided into yes/no answers, backchannels and acknowledgments. In this study, it is the attitude of the response which is the focus of interest. Thus, the cut is instead made between dis-preference, news receiving and general feedback. These are further subdivided into their turn-taking effect: Other speaker, Same speaker and Simultaneous start. This allows us to verify if conversational grunts are simply carriers of prosodic information. In this study, we use a supra-segmental prosodic signal representation based on Time Varying Constant-Q Cepstral Coefficients (TVCQCC) introduced in (Neiberg et al., 2010), for classification and intuitive visualization of feedback and fillers. The contribution of the end of interlocutor left context for predicting turn taking effect has been studied for a while (Duncan, 1972) and is also addressed in this study. In addition, we examine the effect of contextual timing features, which has been shown to be useful in DAs recognition (Laskowski and Shriberg, 2010). We use the Swedish DEAL corpus which has annotated fillers and feedback attitudes. Classification results using linear discriminant analysis are presented. It was found that feedbacks followed by a clean floor taking lose some of their prosodic cues which signal attitude compared to a clean continuer feedback. Turn taking effects can be predicted well over chance level, while Simultaneous Start can’t be predicted at all. However, feedback tokens before Simultaneous Starts were found to be more equal feedback continuers than turn initial feedback tokens, which may be explained as inappropriate floor stealing attempts from the feedback producing speaker. An analysis based on the prototypical spectrograms closely follows the results for Bad News (Dispreference) vs Good news (News reciving) found in Freese and Maynard (1998) although the defnitions differ slightly.</p
Modelling Paralinguistic Conversational Interaction : Towards social awareness in spoken human-machine dialogue
No full textParallel with the orthographic streams of words in conversation are multiple layered epiphenomena, short in duration and with a communicativepurpose. These paralinguistic events regulate the interaction flow via gaze,gestures and intonation. This thesis focus on how to compute, model, discoverand analyze prosody and it’s applications for spoken dialog systems.Specifically it addresses automatic classification and analysis of conversationalcues related to turn-taking, brief feedback, affective expressions, their crossrelationshipsas well as their cognitive and neurological basis. Techniques areproposed for instantaneous and suprasegmental parameterization of scalarand vector valued representations of fundamental frequency, but also intensity and voice quality. Examples are given for how to engineer supervised learned automata’s for off-line processing of conversational corpora as well as for incremental on-line processing with low-latency constraints suitable as detector modules in a responsive social interface. Specific attention is given to the communicative functions of vocal feedback like "mhm", "okay" and "yeah, that’s right" as postulated by the theories of grounding, emotion and a survey on laymen opinions. The potential functions and their prosodic cues are investigated via automatic decoding, data-mining, exploratory visualization and descriptive measurements.QC 20120914</p
Visualizing prosodic densities and contours : Forming one from many
No full textThis paper summarizes a flora of explorative visualization techniques for prosody developed at KTH. It is demonstrated how analysis can be made which goes beyond conventional methodology. Examples are given for turn taking, affective speech, response tokens and Swedish accent II.</p
Online Detection Of Vocal Listener Responses With Maximum Latency Constraints
No full textWhen human listeners utter Listener Responses (e.g. back-channels or acknowledgments) such as 'yeah' and 'mmhmm', interlocutors commonly continue to speak or resume their speech even before the listener has nished his/her response. This type of speech interactivity results in frequent speech overlap which is common in human-human conversation. To allow for this type of speech interactivity to occur between humans and spoken dialog systems, which will result in more human-like continuous and smoother human-machine interaction, we propose an on-line classier which can classify incoming speech as Listener Responses. We show that it is possible to detect vocal Listener Responses using maximum latency thresholds of 100-500 ms, thereby obtaining equal error rates ranging from 34% to 28% by using an energy based voice activity detector
A Dual Channel Coupled Decoder for Fillers and Feedback
No full textThis study presents a dual channel decoder capable of modeling cross-speaker dependencies for segmentation and classification of fillers and feedbacks in conversational speech found in the DEAL corpus. For the same number of Gaussians per state, we have shown improvement in terms of average F-score for the successive addition of 1) increased frame rate from 10 ms to 50 ms 2) Joint Maximum Cross-Correlation (JMXC) features in a single channel decoder 3) a joint transition matrix which captures dependencies symmetrically across the two channels 4) coupled acoustic model retraining symmetrically across the two channels. The final step gives a relative improvement of over 100% for fillers and feedbacks compared to our previous published results. The F-scores are in the range to make it possible to use the decoder as both a voice activity detector and an illucotary act decoder for semi-automatic annotation.</p
The Prosody of Swedish Conversational Grunts
No full textThis paper explores conversational grunts in a face-to-face setting. The study investigates the prosody and turn-taking effect of fillers and feedback tokens that has been annotated for attitudes. The grunts were selected from the DEAL corpus and automatically annotated for their turn taking effect. A novel suprasegmental prosodic signal representation and contextual timing features are used for classification and visualization. Classification results using linear discriminant analysis, show that turn-initial feedback tokens lose some of their attitude-signaling prosodic cues compared to non-overlapping continuer feedback tokens. Turn taking effects can be predicted well over chance level, except Simultaneous Starts. However, feedback tokens before places where both speakers take the turn were more similar to feedback continuers than to turn initial feedback tokens.</p
