Jan Silovský

Two-Level Fusion to Improve Emotion Classification in Spoken Dialogue Systems

This paper proposes a technique to enhance emotion classification in spoken dialogue systems by means of two fusion modules. The first combines emotion predictions generated by a set of classifiers that deal with different kinds of information about each sentence uttered by the user. To do this, the module employs several fusion methods that produce other predictions about the emotional state of the user. The predictions are the input to the second fusion module, where they are combined to deduce the users emotional state. Experiments have been carried out considering two emotion categories (`Non-negative and `Negative) and classifiers that deal with prosodic, acoustic, lexical and dialogue acts information. The results show that the first fusion module significantly increases the classification rates of a baseline and the classifiers working separately, as has been observed previously in the literature. The novelty of the technique is the inclusion of the second fusion module, which enhances classification rate by 2.25% absolute.

Study on Speaker Adaptation Methods in the Broadcast News Transcription Task

This paper deals with the use of speaker adaptation methods in the broadcast news transcription task, which is very difficult from speaker adaptation point of view. It is because in typical broadcast news, speakers change frequently and their identity is not known in the time when the given program is being transcribed. Due to this fact, it is often necessary to use some unconventional speaker adaptation methods here which can operate without the knowledge of speakers identity and/or in an unsupervised mode too. In this paper, we compare and propose several such methods that can operate both in on-line and off-line modes in addition and we show their performance in a real broadcast news transcription system.

Adapting lexical and language models for transcription of highly spontaneous spoken Czech

The paper deals with the problem of automatic transcription of spontaneous conversations in Czech. That type of speech is informal with many colloquial words. It is difficult to create an appropriate lexicon and language model when linguistic resources representing colloquial Czech are limited to several small corpora collected by the Institute of Czech National Corpus. To overcome this, we introduce transformations between the most frequent colloquial words and their counterparts in formal Czech. This allows us a) to combine the small spoken corpora with much larger corpora of more formal texts, b) to optimize the recognizers lexicon, and c) to solve the data sparsity problem when computing a probabilistic language model. We have applied this approach in the design of a system for transcription of spontaneous telephone conversations. Its recent version operates with accuracy about 48% and the proposed transformations together with corpora mixing contributed to 9% improvement compared to the baseline system.