607 research outputs found
The Language of Dreams: Application of Linguistics-Based Approaches for the Automated Analysis of Dream Experiences
The study of dreams represents a crucial intersection between philosophical, psychological, neuroscientific, and clinical interests. Importantly, one of the main sources of insight into dreaming activity are the (oral or written) reports provided by dreamers upon awakening from their sleep. Classically, two main types of information are commonly extracted from dream reports: structural and semantic, content-related information. Extracted structural information is typically limited to the simple count of words or sentences in a report. Instead, content analysis usually relies on quantitative scores assigned by two or more (blind) human operators through the use of predefined coding systems. Within this review, we will show that methods borrowed from the field of linguistic analysis, such as graph analysis, dictionary-based content analysis, and distributional semantics approaches, could be used to complement and, in many cases, replace classical measures and scales for the quantitative structural and semantic assessment of dream reports. Importantly, these methods allow the direct (operator-independent) extraction of quantitative information from language data, hence enabling a fully objective and reproducible analysis of conscious experiences occurring during human sleep. Most importantly, these approaches can be partially or fully automatized and may thus be easily applied to the analysis of large datasets
Unsupervised learning for text-to-speech synthesis
This thesis introduces a general method for incorporating the distributional analysis
of textual and linguistic objects into text-to-speech (TTS) conversion systems.
Conventional TTS conversion uses intermediate layers of representation to bridge
the gap between text and speech. Collecting the annotated data needed to produce
these intermediate layers is a far from trivial task, possibly prohibitively so
for languages in which no such resources are in existence. Distributional analysis,
in contrast, proceeds in an unsupervised manner, and so enables the creation of
systems using textual data that are not annotated. The method therefore aids
the building of systems for languages in which conventional linguistic resources
are scarce, but is not restricted to these languages.
The distributional analysis proposed here places the textual objects analysed
in a continuous-valued space, rather than specifying a hard categorisation of those
objects. This space is then partitioned during the training of acoustic models for
synthesis, so that the models generalise over objects' surface forms in a way that
is acoustically relevant.
The method is applied to three levels of textual analysis: to the characterisation
of sub-syllabic units, word units and utterances. Entire systems for three
languages (English, Finnish and Romanian) are built with no reliance on manually
labelled data or language-specific expertise. Results of a subjective evaluation
are presented
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