Automatic detection of collaboration quality from the students’ speech could support teachers in monitoring group dynamics, diagnosing issues, and developing pedagogical intervention plans.
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.
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.
This work investigates whether nonlexical information from speech can automatically predict the quality of small-group collaborations. Audio was collected from students as they collaborated in groups of three to solve math problems. Experts in education hand-annotated 30-second time windows for collaboration quality. Speech activity features, computed at the group level, and spectral, temporal and prosodic features, extracted at the speaker level, were explored. Fusion on features was also performed after transforming the later ones from the speaker to the group level. Machine learning approaches using Support Vector Machines and Random Forests show that feature fusion yields the best classification performance. The corresponding unweighted average F1 measure on a 4-class prediction task ranges between 40% and 50%, much higher than chance (12%). Speech activity features alone are also strong
predictors of collaboration quality achieving an F1 measure that ranges between 35% and 43%. Spectral, temporal and prosodic features alone achieve the lowest classification performance, but still higher than chance, and exhibit considerable contribution to speech activity feature performance as validated by the fusion results. These novel findings illustrate that the approach under study seems promising for monitoring of group dynamics and attractive in many collaboration activity settings where privacy is desired.
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.
Classification of lexical stress using spectral and prosodic features for computer-assisted language learning systems
We present a system for detection of lexical stress in English words spoken by English learners. This system was designed to be part of the EduSpeak® computer-assisted language learning (CALL) software. The system uses both prosodic and spectral features to detect the level of stress (unstressed, primary or secondary) for each syllable in a word. Features are computed on the vowels and include normalized energy, pitch, spectral tilt, and duration measurements, as well as log-posterior probabilities obtained from the frame-level mel-frequency cepstral coefficients (MFCCs). Gaussian mixture models (GMMs) are used to represent the distribution of these features for each stress class. The system is trained on utterances by L1-English children and tested on English speech from L1-English children and L1-Japanese children with variable levels of English proficiency. Since it is trained on data from L1-English speakers, the system can be used on English utterances spoken by speakers of any L1 without retraining. Furthermore, automatically determined stress patterns are used as the intended target; therefore, hand-labeling of training data is not required. This allows us to use a large amount of data for training the system. Our algorithm results in an error rate of approximately 11% on English utterances from L1-English speakers and 20% on English utterances from L1-Japanese speakers. We show that all features, both spectral and prosodic, are necessary for achievement of optimal performance on the data from L1-English speakers; MFCC log-posterior probability features are the single best set of features, followed by duration, energy, pitch and finally, spectral tilt features. For English utterances from L1-Japanese speakers, energy, MFCC log-posterior probabilities and duration are the most important 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.
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.
We introduce a new database for evaluation of speaker recognition systems.