Deep Learning for Automatic Assessment and Feedback of Spoken English

Growing global demand for learning a second language (L2), particularly English, has led to considerable interest in automatic spoken language assessment, whether for use in computerassisted language learning (CALL) tools or for grading candidates for formal qualifications. This thesis presents research conducted into the automatic assessment of spontaneous nonnative English speech, with a view to be able to provide meaningful feedback to learners. One of the challenges in automatic spoken language assessment is giving candidates feedback on particular aspects, or views, of their spoken language proficiency, in addition to the overall holistic score normally provided. Another is detecting pronunciation and other types of errors at the word or utterance level and feeding them back to the learner in a useful way. It is usually difficult to obtain accurate training data with separate scores for different views and, as examiners are often trained to give holistic grades, single-view scores can suffer issues of consistency. Conversely, holistic scores are available for various standard assessment tasks such as Linguaskill. An investigation is thus conducted into whether assessment scores linked to particular views of the speaker’s ability can be obtained from systems trained using only holistic scores. End-to-end neural systems are designed with structures and forms of input tuned to single views, specifically each of pronunciation, rhythm, intonation and text. By training each system on large quantities of candidate data, individual-view information should be possible to extract. The relationships between the predictions of each system are evaluated to examine whether they are, in fact, extracting different information about the speaker. Three methods of combining the systems to predict holistic score are investigated, namely averaging their predictions and concatenating and attending over their intermediate representations. The combined graders are compared to each other and to baseline approaches. The tasks of error detection and error tendency diagnosis become particularly challenging when the speech in question is spontaneous and particularly given the challenges posed by the inconsistency of human annotation of pronunciation errors. An approach to these tasks is presented by distinguishing between lexical errors, wherein the speaker does not know how a particular word is pronounced, and accent errors, wherein the candidate’s speech exhibits consistent patterns of phone substitution, deletion and insertion. Three annotated corpora x of non-native English speech by speakers of multiple L1s are analysed, the consistency of human annotation investigated and a method presented for detecting individual accent and lexical errors and diagnosing accent error tendencies at the speaker level

Automatic Pronunciation Assessment -- A Review

Pronunciation assessment and its application in computer-aided pronunciation training (CAPT) have seen impressive progress in recent years. With the rapid growth in language processing and deep learning over the past few years, there is a need for an updated review. In this paper, we review methods employed in pronunciation assessment for both phonemic and prosodic. We categorize the main challenges observed in prominent research trends, and highlight existing limitations, and available resources. This is followed by a discussion of the remaining challenges and possible directions for future work.Comment: 9 pages, accepted to EMNLP Finding

Phonetic accommodation to natural and synthetic voices : Behavior of groups and individuals in speech shadowing

The present study investigates whether native speakers of German phonetically accommodate to natural and synthetic voices in a shadowing experiment. We aim to determine whether this phenomenon, which is frequently found in HHI, also occurs in HCI involving synthetic speech. The examined features pertain to different phonetic domains: allophonic variation, schwa epenthesis, realization of pitch accents, word-based temporal structure and distribution of spectral energy. On the individual level, we found that the participants converged to varying subsets of the examined features, while they maintained their baseline behavior in other cases or, in rare instances, even diverged from the model voices. This shows that accommodation with respect to one particular feature may not predict the behavior with respect to another feature. On the group level, the participants of the natural condition converged to all features under examination, however very subtly so for schwa epenthesis. The synthetic voices, while partly reducing the strength of effects found for the natural voices, triggered accommodating behavior as well. The predominant pattern for all voice types was convergence during the interaction followed by divergence after the interaction

Phonetic accommodation of human interlocutors in the context of human-computer interaction

Phonetic accommodation refers to the phenomenon that interlocutors adapt their way of speaking to each other within an interaction. This can have a positive influence on the communication quality. As we increasingly use spoken language to interact with computers these days, the phenomenon of phonetic accommodation is also investigated in the context of human-computer interaction: on the one hand, to find out whether speakers adapt to a computer agent in a similar way as they do to a human interlocutor, on the other hand, to implement accommodation behavior in spoken dialog systems and explore how this affects their users. To date, the focus has been mainly on the global acoustic-prosodic level. The present work demonstrates that speakers interacting with a computer agent also identify locally anchored phonetic phenomena such as segmental allophonic variation and local prosodic features as accommodation targets and converge on them. To this end, we conducted two experiments. First, we applied the shadowing method, where the participants repeated short sentences from natural and synthetic model speakers. In the second experiment, we used the Wizard-of-Oz method, in which an intelligent spoken dialog system is simulated, to enable a dynamic exchange between the participants and a computer agent — the virtual language learning tutor Mirabella. The target language of our experiments was German. Phonetic convergence occurred in both experiments when natural voices were used as well as when synthetic voices were used as stimuli. Moreover, both native and non-native speakers of the target language converged to Mirabella. Thus, accommodation could be relevant, for example, in the context of computer-assisted language learning. Individual variation in accommodation behavior can be attributed in part to speaker-specific characteristics, one of which is assumed to be the personality structure. We included the Big Five personality traits as well as the concept of mental boundaries in the analysis of our data. Different personality traits influenced accommodation to different types of phonetic features. Mental boundaries have not been studied before in the context of phonetic accommodation. We created a validated German adaptation of a questionnaire that assesses the strength of mental boundaries. The latter can be used in future studies involving mental boundaries in native speakers of German.Bei phonetischer Akkommodation handelt es sich um das Phänomen, dass Gesprächspartner ihre Sprechweise innerhalb einer Interaktion aneinander anpassen. Dies kann die Qualität der Kommunikation positiv beeinflussen. Da wir heutzutage immer öfter mittels gesprochener Sprache mit Computern interagieren, wird das Phänomen der phonetischen Akkommodation auch im Kontext der Mensch-Computer-Interaktion untersucht: zum einen, um herauszufinden, ob sich Sprecher an einen Computeragenten in ähnlicher Weise anpassen wie an einen menschlichen Gesprächspartner, zum anderen, um das Akkommodationsverhalten in Sprachdialogsysteme zu implementieren und zu erforschen, wie dieses auf ihre Benutzer wirkt. Bislang lag der Fokus dabei hauptsächlich auf der globalen akustisch-prosodischen Ebene. Die vorliegende Arbeit zeigt, dass Sprecher in Interaktion mit einem Computeragenten auch lokal verankerte phonetische Phänomene wie segmentale allophone Variation und lokale prosodische Merkmale als Akkommodationsziele identifizieren und in Bezug auf diese konvergieren. Dabei wendeten wir in einem ersten Experiment die Shadowing-Methode an, bei der die Teilnehmer kurze Sätze von natürlichen und synthetischen Modellsprechern wiederholten. In einem zweiten Experiment ermöglichten wir mit der Wizard-of-Oz-Methode, bei der ein intelligentes Sprachdialogsystem simuliert wird, einen dynamischen Austausch zwischen den Teilnehmern und einem Computeragenten — der virtuellen Sprachlerntutorin Mirabella. Die Zielsprache unserer Experimente war Deutsch. Phonetische Konvergenz trat in beiden Experimenten sowohl bei Verwendung natürlicher Stimmen als auch bei Verwendung synthetischer Stimmen als Stimuli auf. Zudem konvergierten sowohl Muttersprachler als auch Nicht-Muttersprachler der Zielsprache zu Mirabella. Somit könnte Akkommodation zum Beispiel im Kontext des computergstützten Sprachenlernens zum Tragen kommen. Individuelle Variation im Akkommodationsverhalten kann unter anderem auf sprecherspezifische Eigenschaften zurückgeführt werden. Es wird vermutet, dass zu diesen auch die Persönlichkeitsstruktur gehört. Wir bezogen die Big Five Persönlichkeitsmerkmale sowie das Konzept der mentalen Grenzen in die Analyse unserer Daten ein. Verschiedene Persönlichkeitsmerkmale beeinflussten die Akkommodation zu unterschiedlichen Typen von phonetischen Merkmalen. Die mentalen Grenzen sind im Zusammenhang mit phonetischer Akkommodation zuvor noch nicht untersucht worden. Wir erstellten eine validierte deutsche Adaptierung eines Fragebogens, der die Stärke der mentalen Grenzen erhebt. Diese kann in zukünftigen Untersuchungen mentaler Grenzen bei Muttersprachlern des Deutschen verwendet werden.Deutsche Forschungsgemeinschaft (DFG) – Projektnummer 278805297: "Phonetische Konvergenz in der Mensch-Maschine-Kommunikation

Automatic Speech Recognition for Low-resource Languages and Accents Using Multilingual and Crosslingual Information

This thesis explores methods to rapidly bootstrap automatic speech recognition systems for languages, which lack resources for speech and language processing. We focus on finding approaches which allow using data from multiple languages to improve the performance for those languages on different levels, such as feature extraction, acoustic modeling and language modeling. Under application aspects, this thesis also includes research work on non-native and Code-Switching speech

Using challenges to enhance a learning game for pronunciation training of English as a second language

Producción CientíficaLearning games have a remarkable potential for education. They provide an emergent form of social participation that deserves the assessment of their usefulness and efficiency in learning processes. This study describes a novel learning game for foreign pronunciation training in which players can challenge each other. Native Spanish speakers performed several pronunciation activities during a one-month competition using a mobile application, designed under a minimal pairs approach, to improve their pronunciation of English as a foreign language. This game took place in a competitive scenario in which students had to challenge other participants in order to get high scores and climb up a leaderboard. Results show intense practice supported by a significant number of activities and playing regularity, so the most active and motivated players in the competition achieved significant pronunciation improvement results. The integration of automatic speech recognition (ASR) and text-to-speech (TTS) technology allowed users to improve their pronunciation while being immersed in a highly motivational game.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (grant TIN2014-59852-R)Junta de Castilla y Leon (grant VA050G18

Proceedings of the VIIth GSCP International Conference

The 7th International Conference of the Gruppo di Studi sulla Comunicazione Parlata, dedicated to the memory of Claire Blanche-Benveniste, chose as its main theme Speech and Corpora. The wide international origin of the 235 authors from 21 countries and 95 institutions led to papers on many different languages. The 89 papers of this volume reflect the themes of the conference: spoken corpora compilation and annotation, with the technological connected fields; the relation between prosody and pragmatics; speech pathologies; and different papers on phonetics, speech and linguistic analysis, pragmatics and sociolinguistics. Many papers are also dedicated to speech and second language studies. The online publication with FUP allows direct access to sound and video linked to papers (when downloaded)

Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition

This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network