Panayiotis Georgiou

Apple (United Kingdom)

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i-10 index

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Co-Authors

Shrikanth Narayanan

University of Southern California

156

Brian Baucom

University of Utah

David C. Atkins

Consumer VOICE

Zac E. Imel

University of Utah

Sandeep Nallan Chakravarthula

University of Southern California

James Gibson

Active Signal Technologies (United States)

Shao-Yen Tseng

Intel (United States)

Prashanth Gurunath Shivakumar

Amazon (United States)

Md Nasir

Microsoft (United States)

Athanasios Katsamanis

National Technical University of Athens

Cooperative Institutions

University of Southern California

113

Southern California University for Professional Studies

University of Washington

University of Pennsylvania

Health Services Research & Development

University of Utah

University of California, Los Angeles

Seattle University

Washington University in St. Louis

Temple University Health System

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Physician assessment of asthma control in patients receiving omalizumab in a real-world setting

Melinda Zsuzsanna Szabó J Chlumský Robert Maykut Panayiotis Georgiou Chien‐Wei Chen

GINA guidelines define controlled, partly controlled and uncontrolled asthma based on a number of individual components (symptoms, activity limitation, rescue medication use, lung function and exacerbations). The 2-year, global, single-arm, observational eXpeRience registry evaluated the efficacy and safety of omalizumab (OMA) in patients with allergic asthma in a 9real-world9 setting. Investigators were instructed to assess asthma control using the GINA 2006 definitions. A post-hoc analysis compiled the individual components of control recorded by physicians and applied these to the GINA definitions. The intent-to-treat population consisted of 916 patients. Investigators9 assessment (IA) of control increased from 1.4% at baseline to 41.1% after 2 years of treatment (Table). Asthma control by strict GINA definitions increased from 0.8% at baseline to 21.0% over the 2 year treatment period (Table). These data show differences between physician9s assessment of asthma control and control as determined by strict application of GINA 2006 definitions. This study did not explore what determined the differences in assessment, which may be of interest for future study. Regardless of method, asthma control improved over time in patients receiving OMA in this real-world study.

Omalizumab

Post hoc

Post-hoc analysis

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Analyzing the Language of Therapist Empathy in Motivational Interview based Psychotherapy.

PubMed (2012)

Bo Xiao Doğan Can Panayiotis Georgiou David C. Atkins Shrikanth Narayanan

Empathy is an important aspect of social communication, especially in medical and psychotherapy applications. Measures of empathy can offer insights into the quality of therapy. We use an N-gram language model based maximum likelihood strategy to classify empathic versus non-empathic utterances and report the precision and recall of classification for various parameters. High recall is obtained with unigram while bigram features achieved the highest F1-score. Based on the utterance level models, a group of lexical features are extracted at the therapy session level. The effectiveness of these features in modeling session level annotator perceptions of empathy is evaluated through correlation with expert-coded session level empathy scores. Our combined feature set achieved a correlation of 0.558 between predicted and expert-coded empathy scores. Results also suggest that the longer term empathy perception process may be more related to isolated empathic salient events.

Utterance

Feature (linguistics)

Source

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Citations (53)

A transcription scheme for languages employing the Arabic script motivated by speech processing application

Shadi Ganjavi Panayiotis Georgiou Shrikanth Narayanan

This paper offers a transcription system for Persian, the target language in the Transonics project, a speech-to-speech translation system developed as a part of the DARPA Babylon program (The DARPA Babylon Program; Narayanan, 2003). In this paper, we discuss transcription systems needed for automated spoken language processing applications in Persian that uses the Arabic script for writing. This system can easily be modified for Arabic, Dari, Urdu and any other language that uses the Arabic script. The proposed system has two components. One is a phonemic based transcription of sounds for acoustic modelling in Automatic Speech Recognizers and for Text to Speech synthesizer, using ASCII based symbols, rather than International Phonetic Alphabet symbols. The other is a hybrid system that provides a minimally-ambiguous lexical representation that explicitly includes vocalic information; such a representation is needed for language modelling, text to speech synthesis and machine translation.

Transcription

ASCII

Phonetic transcription

Speech translation

Writing system

10.3115/1621804.1621821

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Citations (2)

Linguistically Aided Speaker Diarization Using Speaker Role Information

Nikolaos Flemotomos Panayiotis Georgiou Shrikanth Narayanan

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.

Speaker diarisation

10.21437/odyssey.2020-17

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Citations (19)

Empirical link between hypothesis diversity and fusion performance in an ensemble of automatic speech recognition systems

Interspeech 2022 (2013)

Kartik Audhkhasi Andreas M. Zavou Panayiotis Georgiou Shrikanth Narayanan

Link (geometry)

Empirical Research

10.21437/interspeech.2013-672

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Citations (8)

Towards modeling user behavior in interactions mediated through an automated bidirectional speech translation system

Computer Speech & Language (2009)

Jongho Shin Panayiotis Georgiou Shrikanth Narayanan

Speech translation

10.1016/j.csl.2009.04.008

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Citations (6)

Peer Review #1 of "A technology prototype system for rating therapist empathy from audio recordings in addiction counseling (v0.2)"

Bo Xiao Che-Wei Huang Zac E. Imel David C. Atkins Panayiotis Georgiou

Scaling up psychotherapy services such as for addiction counseling is a critical societal need.One challenge is ensuring quality of therapy, due to the heavy cost of manual observational assessment.This work proposes a speech technology based system to automate the assessment of therapist empathy ---a key therapy quality index ---from audio recordings of the psychotherapy interactions.We design a speech processing system that includes voice activity detection and diarization modules, and an automatic speech recognizer plus a speaker role matching module to extract the therapist's language cues.We employ Maximum Entropy models, Maximum Likelihood language models, and a Lattice Rescoring method to characterize high vs. low empathic language.We estimate therapy-session level empathy codes using utterance level evidence obtained from these models.Our experiments showed that the fully automated system achieved a correlation of 0.643 between expert annotated empathy codes and machine-derived estimations, and an accuracy of 81% in classifying high vs. low empathy, in comparison to a 0.721 correlation and 86% accuracy in the oracle setting using manual transcripts.The results show that the system provides useful information that can contribute to automatic quality insurance and therapist training.

10.7287/peerj-cs.59v0.2/reviews/1

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Peer Review #2 of "A technology prototype system for rating therapist empathy from audio recordings in addiction counseling (v0.1)"

Bo Xiao Che-Wei Huang Zac E. Imel David C. Atkins Panayiotis Georgiou

10.7287/peerj-cs.59v0.1/reviews/2

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"Honey, I Learned to Talk"

Shao-Yen Tseng Haoqi Li Brian Baucom Panayiotis Georgiou

In this work we analyze the importance of lexical and acoustic modalities in behavioral expression and perception. We demonstrate that this importance relates to the amount of therapy, and hence communication training, that a person received. It also exhibits some relationship to gender. We proceed to provide an analysis on couple therapy data by splitting the data into clusters based on gender or stage in therapy. Our analysis demonstrates the significant difference between optimal modality weights per cluster and relationship to therapy stage. Given this finding we propose the use of communication-skill aware fusion models to account for these differences in modality importance. The fusion models operate on partitions of the data according to the gender of the speaker or the therapy stage of the couple. We show that while most multimodal fusion methods can improve mean absolute error of behavioral estimates, the best results are given by a model that considers the degree of communication training among the interlocutors.

Modalities

Modality (human–computer interaction)

Sensor Fusion

Degree (music)

10.1145/3242969.3242996

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Citations (11)

Transfer Learning from Adult to Children for Speech Recognition: Evaluation, Analysis and Recommendations

arXiv (Cornell University) (2018)

Prashanth Gurunath Shivakumar Panayiotis Georgiou

Children speech recognition is challenging mainly due to the inherent high variability in children's physical and articulatory characteristics and expressions. This variability manifests in both acoustic constructs and linguistic usage due to the rapidly changing developmental stage in children's life. Part of the challenge is due to the lack of large amounts of available children speech data for efficient modeling. This work attempts to address the key challenges using transfer learning from adult's models to children's models in a Deep Neural Network (DNN) framework for children's Automatic Speech Recognition (ASR) task evaluating on multiple children's speech corpora with a large vocabulary. The paper presents a systematic and an extensive analysis of the proposed transfer learning technique considering the key factors affecting children's speech recognition from prior literature. Evaluations are presented on (i) comparisons of earlier GMM-HMM and the newer DNN Models, (ii) effectiveness of standard adaptation techniques versus transfer learning, (iii) various adaptation configurations in tackling the variabilities present in children speech, in terms of (a) acoustic spectral variability, and (b) pronunciation variability and linguistic constraints. Our Analysis spans over (i) number of DNN model parameters (for adaptation), (ii) amount of adaptation data, (iii) ages of children, (iv) age dependent-independent adaptation. Finally, we provide Recommendations on (i) the favorable strategies over various aforementioned - analyzed parameters, and (ii) potential future research directions and relevant challenges/problems persisting in DNN based ASR for children's speech.

Pronunciation

Transfer of learning

10.48550/arxiv.1805.03322

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Citations (1)