Showing papers by "R. De Mori published in 2008"

Spoken language understanding

Abstract: Semantics deals with the organization of meanings and the relations between sensory signs or symbols and what they denote or mean. Computational semantics performs a conceptualization of the world using computational processes for composing a meaning representation structure from available signs and their features present, for example, in words and sentences. Spoken language understanding (SLU) is the interpretation of signs conveyed by a speech signal. SLU and natural language understanding (NLU) share the goal of obtaining a conceptual representation of natural language sentences. Specific to SLU is the fact that signs to be used for interpretation are coded into signals along with other information such as speaker identity. Furthermore, spoken sentences often do not follow the grammar of a language; they exhibit self-corrections, hesitations, repetitions, and other irregular phenomena. SLU systems contain an automatic speech recognition (ASR) component and must be robust to noise due to the spontaneous nature of spoken language and the errors introduced by ASR. Moreover, ASR components output a stream of words with no structure information like punctuation and sentence boundaries. Therefore, SLU systems cannot rely on such markers and must perform text segmentation and understanding at the same time.

Frame-based acoustic feature integration for speech understanding

Abstract: With the purpose of improving spoken language understanding (SLU) performance, a combination of different acoustic speech recognition (ASR) systems is proposed. State a posteriori probabilities obtained with systems using different acoustic feature sets are combined with log-linear interpolation. In order to perform a coherent combination of these probabilities, acoustic models must have the same topology (i.e. same set of states). For this purpose, a fast and efficient twin model training protocol is proposed. By a wise choice of acoustic feature sets and log-linear interpolation of their likelihood ratios, a substantial concept error rate (CER) reduction has been observed on the test part of the French MEDIA corpus.

Semantic composition process in a speech understanding system

Abstract: A knowledge representation formalism for SLU is introduced. It is used for incremental and partially automated annotation of the Media corpus in terms of semantic structures. An automatic interpretation process is described for composing semantic structures from basic semantic constituents using patterns involving constituents and words. The process has procedures for obtaining semantic compositions and for generating frame hypotheses by inference. This process is evaluated on a dialogue corpus manually annotated at the word and semantic constituent levels.

Speaker turn characterization for spoken dialog system monitoring and adaptation

Abstract: This paper describes an utterance classification method based on a multiple decoding scheme. We use the Spoken Language Understanding (SLU) strategy proposed within the European project LUNA. The goal of this classification process is to characterize each speaker's turn, in a dialog context, according to different categories relevant from an SLU point of view: out-of-domain messages, requests not covered by the interpretation module, frequent requests,.... These categories are used for two purposes in an off-line mode: system monitoring for detecting changes in users' behaviour and system adaptation by selecting dialogs likely to contain some phenomenon poorly covered by the models for an active learning scheme. All the models and the evaluations are performed on the France Telecom FT3000 corpus.