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Language is a structured system of communication that consists of grammar and vocabulary. It is the primary means by which humans convey meaning, both in spoken and written forms, and may also be conveyed through sign languages. The vast majority of human languages have developed writing systems that allow for the recording and preservation of the sounds or signs of language. Human language is characterized by its cultural and historical diversity, with significant variations observed between cultures and across time. Human languages possess the properties of productivity and displacement, which enable the creation of an infinite number of sentences, and the ability to refer to objects, events, and ideas that are not immediately present in the discourse. The use of human language relies on social convention and is acquired through learning. Estimates of the number of human languages in the world vary between and . Precise estimates depend on an arbitrary distincti

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Multilingual and Unsupervised Subword Modeling for Zero-Resource Languages

Enno Hermann

Subword modeling for zero-resource languages aims to learn low-level representations of speech audio without using transcriptions or other resources from the target language (such as text corpora or pronunciation dictionaries). A good representation should capture phonetic content and abstract away from other types of variability, such as speaker differences and channel noise. Previous work in this area has primarily focused unsupervised learning from target language data only, and has been evaluated only intrinsically. Here we directly compare multiple methods, including some that use only target language speech data and some that use transcribed speech from other (non-target) languages, and we evaluate using two intrinsic measures as well as on a downstream unsupervised word segmentation and clustering task. We find that combining two existing target-language-only methods yields better features than either method alone. Nevertheless, even better results are obtained by extracting target language bottleneck features using a model trained on other languages. Cross-lingual training using just one other language is enough to provide this benefit, but multilingual training helps even more. In addition to these results, which hold across both intrinsic measures and the extrinsic task, we discuss the qualitative differences between the different types of learned features.

2020

The Operational Semantics and Implementation of a Core Dart language

Zhivka Gucevska

In this report we present the specification of the operational semantics of Dart Kernel and a reference implementation of Dart Kernel in Dart. We design a CESK-like machine to specify the operational semantics of Dart Kernel and we implement an interpreter that follows closely the small-step semantics of the language. This approach allows us to define the behaviour of the rich features of Dart Kernel, such as exception handling and asynchronous execution. Since Dart Kernel is an evolving language, the specification is expected to evolve with it and the semantics for new features needs to be defined further. The work done for this project sets the grounds for further formalization of the behaviour of the language with a formal proof management system, as Coq.

2017

Integrating Language Identification to improve Multilingual Speech Recognition

Holger Caesar

The process of determining the language of a speech utterance is called Language Identification (LID). This task can be very challenging as it has to take into account various language-specific aspects, such as phonetic, phonotactic, vocabulary and grammar-related cues. In multilingual speech recognition we try to find the most likely word sequence that corresponds to an utterance where the language is not known a priori. This is a considerably harder task compared to monolingual speech recognition and it is common to use LID to estimate the current language. In this project we present two general approaches for LID and describe how to integrate them into multilingual speech recognizers. The first approach uses hierarchical multilayer perceptrons to estimate language posterior probabilities given the acoustics in combination with hidden Markov models. The second approach evaluates the output of a multilingual speech recognizer to determine the spoken language. The research is applied to the MediaParl speech corpus that was recorded at the Parliament of the canton of Valais, where people switch from Swiss French to Swiss German or vice versa. Our experiments show that, on that particular data set, LID can be used to significantly improve the performance of multilingual speech recognizers. We will also point out that ASR dependent LID approaches yield the best performance due to higher-level cues and that our systems perform much worse on non-native data

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The objective of this course is to present the main models, formalisms and algorithms necessary for the development of applications in the field of natural language information processing. The concepts introduced during the lectures will be applied during practical sessions.

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