Prosody—suprasegmental characteristics of speech such as pitch, rhythm, and loudness— is a rich source of information in spoken language and can tell a listener much about the internal state of a speaker. This thesis explores the role of prosody in conveying three very different types of speaker state: paralinguistic state, in particular emotion; pragmatic state, in particular questioning; and the state of spoken language proficiency of non-native English speakers. Paralinguistics. Intonational features describing pitch contour shape were found to dis-criminate emotion in terms of positive and negative affect. A procedure is described for clustering groups of listeners according to perceptual emotion ratings that foster further understanding of the relationship between acoustic-prosodic cues and emotion perception. Pragmatics. Student questions in a corpus of one-on-one tutorial dialogs were found to be signaled primarily by phrase-final rising intonation, an important cue used in conjunc-tion with lexico-pragmatic cues to differentiate the high rate of observed declarative questions from proper declaratives. The automatic classification of question form and

This paper investigates the feasibility of automated scoring of spoken English proficiency of non-native speakers. Unlike existing automated assessments of spoken English, our data consists of spontaneous spoken responses to complex test items. We first compute a set of features relevant for measuring communicative competence based on speech recognition output. We then perform both a quantitative and a qualitative analysis of these features using two different machine learning approaches. (1) We use support vector machines to produce a score and evaluate it with respect to a mode baseline and to human rater agreement. We find that scoring based on support vector machines yields accuracies approaching inter-rater agreement in some cases. (2) We use classification and regression trees to understand the role of different features and feature classes in the characterization of speaking proficiency by human scorers. Our analysis shows that across all the test items most or all the feature classes are used in the nodes of the trees suggesting that the scores are, appropriately, a combination of multiple components of speaking proficiency. Future research will concentrate on extending the set of features and introducing new feature classes to arrive at a scoring model that comprises additional relevant aspects of speaking proficiency.

mispronunciation detection and diagnosis of learners ’ speech with

Proficiency testing is an important ingredient in successful language teaching. However, repeated testing for course placement, over the course of instruction or for certification can be time-consuming and costly. We present the design and validation of the Versant Arabic Test, a fully automated test of spoken Modern Standard Arabic, that evaluates test-takers ' facility in listening and speaking. Experimental data shows the test to be highly reliable (testretest r=0.97) and to strongly predict performance on the ILR OPI (r=0.87), a standard interview test that assesses oral proficiency. 1

This paper describes a system aimed at automatically scoring two task types of high and medium-high linguistic entropy from a spoken English test with a total of six widely differing task types. We describe the speech recognizer used for this system and its acoustic model and language model adaptation; the speech features computed based on the recognition output; and finally the scoring models based on multiple regression and classification trees. For both tasks, agreement measures between machine and human scores (correlation, kappa) are close to or reach inter-human agreements. 1

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processing. This chapter presents these and their combination, followed by some related technologies. 3.1 SPEECH PROCESSING Modern speech technology is based on digital signal processing, probabilistic theory and search algorithms. These techniques make it possible to perform significant data reduction for coding and transmission of speech signals, speech synthesis and automatic recognition of speech, speaker or language. In this section the state-of-the-art is presented and related to realistic military applications. 3.1.1 Speech Coding When digital systems became available, it was obvious that the transmission of digital signals was more efficient than the transmission of analogue signals. If analogue signals are transmitted under adverse conditions, it is not easy to reconstruct the received signal, because the possible signal values are not known in advance. For digital signals discrete levels are used. This allows, within certain limits, the reconstruction of distorted signals. The first digital transmission systems were based on coding the waveform of the speech signal. This results in bit rates between 8000 to 64000 Bps (bits per second). The higher the bit rate the better the quality. Later, more advanced coding systems were used where basic properties of the speech were determined and encoded, resulting in a more efficient coding (bit rates