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This paper reports our initial experiments with automatic punctuation annotation from speech. We have focused on Czech broadcast news speech. The task can be defined as a classification of each inter-word boundary into one of target classes. We considered comma, sentence boundary and “no punctuation” as the target classes. We employed two statistical models(More)
The aim of this paper is to present an extension of the hidden vector state semantic parser. First, we describe the statistical semantic parsing and its decomposition into the semantic and the lexical model. Subsequently, we present the original hidden vector state parser. Then, we modify its lexical model so that it supports the use of the input sequence(More)
We summarize the involvement of our CEMI team in the “NLI Shared Task 2017”, which deals with both textual and speech input data. We submitted the results achieved by using three different system architectures; each of them combines multiple supervised learning models trained on various feature sets. As expected, better results are achieved with the systems(More)
The main objective of the work presented in this paper was to develop a complete system that would accomplish the original visions of the MALACH project. Those goals were to employ automatic speech recognition and information retrieval techniques to provide improved access to the large video archive containing recorded testimonies of the Holocaust(More)
The study offers measurements of the vocal breaks occurring from the modal to the falsetto register in an untrained barytone. The breaks were achieved by increasing the expired airflow. An analogy with a labial pipe served as a help. The voice phenomena were divided into three regions. In the region of low modal frequencies (A-e) a simultaneous sounding of(More)
The hidden vector state (HVS) parser is a popular method for semantic parsing. It is used in the language understanding module of the statistical based spoken dialog system. This paper presents an extension of the HVS semantic parser. It enables the parser to generate broader class of semantic trees. This modification can be used to improve the performance(More)
In this paper, we present a novel method for term score estimation. The method is primarily designed for scoring the out-of-vocabulary terms, however it could also estimate scores for in-vocabulary results. The term score is computed as a cosine distance of two pronunciation embeddings. The first one is generated from the grapheme representation of the(More)