Automatic detection of collaboration quality from the students’ speech could support teachers in monitoring group dynamics, diagnosing issues, and developing pedagogical intervention plans. To address the challenge of mapping characteristics of individuals’ speech to information about the group, we coded behavioral and learning-related indicators of collaboration at the individual level. In this work, we investigate the feasibility of predicting the quality of collaboration among a group of students working together to solve a math problem from human-labelled collaboration indicators. We use a corpus of 6th, 7th, and 8th grade students working in groups of three to solve math problems collaboratively. Researchers labelled both the group-level collaboration quality during each problem and the student-level collaboration indicators. Results using random forests reveal that the individual indicators of collaboration aid in the prediction of group collaboration quality.
Harry Bratt
SRI Author
SenSay Analytics™: A real-time speaker-state platform
Growth in voice-based applications and personalized systems has led to increasing demand for speech- analytics technologies that estimate the state of a speaker from speech. Such systems support a wide range of applications, from more traditional call-center monitoring, to health monitoring, to human-robot interactions, and more. To work seamlessly in real-world contexts, such systems must meet certain requirements, including for speed, customizability, ease of use, robustness, and live integration of both acoustic and lexical cues. This demo introduces SenSay AnalyticsTM, a platform that performs real-time speaker-state classification from spoken audio. SenSay is easily configured and is customizable to new domains, while its underlying architecture offers extensibility and scalability.
Toward human-assisted lexical unit discovery without text resources
This work addresses lexical unit discovery for languages without (usable) written resources. Previous work has addressed this problem using entirely unsupervised methodologies. Our approach in contrast investigates the use of linguistic and speaker knowledge which are often available even if text resources are not. We create a framework that benefits from such resources, not assuming orthographic representations and avoiding generation of word-level transcriptions. We adapt a universal phone recognizer to the target language and use it to convert audio into a searchable phone string for lexical unit discovery via fuzzy sub-string matching. Linguistic knowledge is used to constrain phone recognition output and to constrain lexical unit discovery on the phone recognizer output.
Target language speakers are used to assist a linguist in creating phonetic transcriptions for the adaptation of acoustic and language models, by respeaking more clearly a small portion of the target language audio. We also explore robust features and feature transform through deep auto-encoders for better phone recognition performance.
The proposed approach achieves lexical unit discovery performance comparable to state-of-the-art zero-resource methods. Since the system is built on phonetic recognition, discovered units are immediately interpretable. They can be used to automatically populate a pronunciation lexicon and enable iterative improvement through additional feedback from target language speakers.
The SRI speech-based collaborative learning corpus
We introduce the SRI speech-based collaborative learning corpus, a novel collection designed for the investigation and measurement of how students collaborate together in small groups. This is a multi-speaker corpus containing high-quality audio recordings of middle school students working in groups of three to solve mathematical problems. Each student was recorded via a head-mounted noise-cancelling microphone. Each group was also recorded via a stereo microphone placed nearby. A total of 80 sessions were collected with the participation of 134 students. The average duration of a session was 20 minutes. All students spoke English; for some students, English was a second language. Sessions have been annotated with time stamps to indicate which mathematical problem the students were solving and which student was speaking. Sessions have also been hand annotated with common indicators of collaboration for each speaker (e.g., inviting others to contribute, planning) and the overall collaboration quality for each problem. The corpus will be useful to education researchers interested in collaborative learning and to speech researchers interested in children’s speech, speech analytics, and speech diarization. The corpus, both audio and annotation, will be made available to researchers.
Open Language Interface for Voice Exploitation (OLIVE)
We propose to demonstrate the Open Language Interface for Voice Exploitation (OLIVE) speech-processing system, which SRI International developed under the DARPA Robust Automatic Transcription of Speech (RATS) program. The technology underlying OLIVE was designed to achieve robustness to high levels of noise and distortion for speech activity detection (SAD), speaker identification (SID), language and dialect identification (LID), and keyword spotting (KWS). Our demonstration will show OLIVE performing those four tasks. We will also demonstrate SRI’s speaker recognition capability live on a mobile phone for visitors to interact with.
Spoken Interaction Modeling for Automatic Assessment of Collaborative Learning
Collaborative learning is a key skill for student success, but simultaneous monitoring of multiple small groups is untenable for teachers. This study investigates whether automatic audio- based monitoring of interactions can predict collaboration quality. Data consist of hand-labeled 30-second segments from audio recordings of students as they collaborated on solving math problems. Two types of features were explored: speech activity features, which were computed at the group level; and prosodic features (pitch, energy, durational, and voice quality patterns), which were computed at the speaker level. For both feature types, normalized and unnormalized versions were investigated; the latter facilitate real-time processing applications. Results using boosting classifiers, evaluated by F-measure and accuracy, reveal that (1) both speech activity and prosody features predict quality far beyond chance using majority-class approach; (2) speech activity features are the better predictors overall, but class performance using prosody shows potential synergies; and (3) it may not be necessary to session-normalize features by speaker. These novel results have impact for educational settings, where the approach could support teachers in the monitoring of group dynamics, diagnosis of issues, and development of pedagogical intervention plans.
Adaptive and Discriminative Modeling for Improved Mispronunciation Detection
In the context of computer-aided language learning, automatic detection of specific phone mispronunciations by nonnative speakers can be used to provide detailed feedback about specific pronunciation problems. In previous work we found that significant improvements could be achieved, compared to standard approaches that compute posteriors with respect to native models, by explicitly modeling both mispronunciations and correct pronunciations by nonnative speakers. In this work, we extend our approach with the use of model adaptation and discriminative modeling techniques, inspired on methods that have been effective in the area of speaker identification. Two systems were developed, one based on Bayesian adaptation of Gaussian Mixture Models (GMMs), and likelihood-ratio-based detection, and another one based on Support Vector Machines classification of supervectors derived from adapted GMMs. Both systems, and their combination, were evaluated in a phonetically transcribed Spanish database of 130,000 phones uttered in continuous speech sentences by 206 nonnative speakers, showing significant improvements from our previous best system.
Lexical Stress Classification for Language Learning Using Spectral and Segmental Features
We present a system for detecting lexical stress in English words spoken by English learners. The system uses both spectral and segmental features to detect three levels of stress for each syllable in a word. The segmental features are computed on the vowels and include normalized energy, pitch, spectral tilt and duration measurements. The spectral features are computed at the frame level and are modeled by one Gaussian Mixture Model (GMM) for each stress class. These GMMs are used to obtain segmental posteriors, which are then appended to the segmental features to obtain a final set of GMMs. The segmental GMMs are used to obtain posteriors for each stress class. The system was tested on English speech from native English-speaking children and from Japanese-speaking children with variable levels of English proficiency. Our algorithm results in an error rate of approximately 13% on native data and 20%on Japanese non-native data.
