Acoustic anger detection in voice portals can help to enhance human computer interaction. A comprehensive voice portal data collection has been carried out and gives new insight on the nature of real life data. Manual labeling revealed a high percentage of non-classifiable data. Experiments with a statistical classifier indicate that, in contrast to pitch and energy related features, duration measures do not play an important role for this data while cepstral information does. Also in a direct comparison between Gaussian Mixture Models and Support Vector Machines the latter gave better results.

This paper presents a comparative study of four different approaches to automatic age and gender classification using seven classes on a telephony speech task and also compares the results with human performance on the same data. The automatic approaches compared are based on (1) a parallel phone recognizer, derived from an automatic language identification system; (2) a system using dynamic Bayesian networks to combine several prosodic features; (3) a system based solely on linear prediction analysis; and (4) Gaussian mixture models based on MFCCs for separate recognition of age and gender. On average, the parallel phone recognizer performs as well as Human listeners do, while loosing performance on short utterances. The system based on prosodic features however shows very little dependence on the length of the utterance.

In the focus of this paper is a comparison of the most relevant prosodic features/feature classes for the classification of boundaries and accents in German and in English.Principal components were computed based on a large prosodic feature vector; these principal components were used as predictor variables in a Linear Discriminant analysis as well as in a Classification and Regression Tree.The number of the most relevant principal components was between three and five; for both languages and for boundary and accent classification alike, most important were principal components modelling duration, in combination with energy, followed by pauses and F0.

Nowadays modern automatic dialogue systems are able to understand complex sentences instead of only a few commands like Stop or No.In a call-center, such a system should be able to determine in a critical phase of the dialogue if the call should be passed over to a human operator.Such a critical phase can be indicated by the customer's vocal expression.Other studies prooved that it is possible to distinguish between anger and neutral speech w i t h prosodic features alone.Subjects in these studies were mostly people acting or simulating emotions like anger.In this paper we use data from a so-called Wizard of O z (WoZ) scenario to get more realistic data instead of simulated anger.As shown below, the classi cation rate for the two classes "emotion" (class E) and "neutral" (class :E) is signi cantly worse for these more realistic data.Furthermore the classi cation results are heavily speaker dependent.Prosody alone might t h us not be sucient and has to be supplemented by the use of other knowledge sources such as the detection of repetitions, reformulations, swear words, and dialogue acts.

During the last years, we have been working on the automatic classification of boundaries and accents in the German VERBMOBIL (VM) project (human-human communication, appointment scheduling dialogues). A sub-corpus was annotated manually with prosodic boundary and accent labels, and neural networks (NN) trained with a large set of prosodic features were used for automatic classification. The classification of boundaries could be improved markedly with a combination of the NN with a language model (LM) that was trained with manually annotated syntactic-prosodic boundary labels in a much larger sub-corpus. Here we show how a combination of NN with LM along similar lines can be used for an improvement of accent classification as well. For the training of the LM, accents are annotated automatically in the transliteration with the help of a rule--based system that uses part--of--speech (POS) as well as other linguistic /phonological information. 1. INTRODUCTION This research has been condu...

We present a n i n tegrated approach for the interpretation of word hypotheses graphs (WHGs) using multiple knowledge sources.Commonly, dierent knowledge sources in speech understanding are applied sequentially.Typically, speech understanding systems, such as the Verbmobil speech-to-speech translation system, rst use a word recognizer to determine word hypotheses, only based on acoustic and language model (LM) information.The resulting word sequences or WHGs are then segmented according to syntactic and/or prosodic information.Finally, these segments are interpreted by a parser or a stochastic process.Thus, it is impossible to use the knowledge of the syntactic-prosodic process, the parser or any other subsequent process to nd the best word sequence.In our new approach w e use acoustic, prosodic and LM information to determine the best word chain, to detect syntactic/prosodic/pragmatic phrase boundaries and to classify dialog acts in one integrated search procedure, based on a WHG or a word lattice.

Prosody can be applied to improve the performance of spontaneous speech translation systems like VERBMOBIL.In VERB-MOBIL we previously augmented the output of a word recognizer with prosodic information.Here we present a new approach of interleaving word recognition and prosodic processing.While we still use the output of a word recognizer to determine phrase boundaries, we do not wait until the end of the utterance before we start processing.Instead we intercept chunks of word hypotheses during the forward search of the recognizer.Neural networks and language models are used to predict phrase boundaries.Those boundary hypotheses, in turn, are used by the recognizer to cut the stream of incoming speech into syntactic-prosodic phrases.Thus, incremental processing is possible.We investigate which features are suited for incremental prosodic processing and compare them w.r.t.classification performance and efficiency.We show that with a set of features that can be computed efficiently classification results are achieved which are almost as good as those with the previously used computationally more expensive features.

In this paper we present an integrated approach for recognizing both the word sequence and the syntactic-prosodic structure of a spontaneous utterance. We take into account the fact that a spontaneous utterance is not merely an unstructured sequence of words by incorporating phrase boundary information into the language model and by providing HMMs to model boundaries. This allows for a distinction between word transitions across phrase boundaries and transitions within a phrase. During recognition, the syntactic-prosodic structure of the utterance is determined implicitly. Without any increase in computational effort, this leads to a 4% reduction of word error rate, and, at the same time, syntactic-prosodic boundary labels are provided for subsequent processing. The boundaries are recognized with a precision and recall rate of about 75% each. They can be used to reduce drastically the computational effort for parsing spontaneous utterances. We also present a system architecture to inco...