Publication

Automatic Speech Recognition and Translation of a Swiss German Dialect: Walliserdeutsch

2014
Conference Papers

Abstract

Walliserdeutsch is a Swiss German dialect spoken in the south west of Switzerland. To investigate the potential of automatic speech processing of Walliserdeutsch, a small database was collected based mainly on broadcast news from a local radio station. Experiments suggest that automatic speech recognition is feasible: use of another (Swiss German) database shows that the small data size lends itself to bootstrapping from other data; use of Kullback-Leibler HMM suggests that phoneme mapping techniques can compensate for a grapheme-based dictionary. Experiments also indicate that statistical machine translation is feasible; the difficulty of small data size is offset by the close proximity to (high) German.

Official source

https://infoscience.epfl.ch/entities/publication/25c9671f-9fa6-4c65-8145-73b3da1268b4

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2014
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/25c9671f-9fa6-4c65-8145-73b3da1268b4

About this result

Related concepts (32)

Machine translation

Machine translation is use of either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches to translation of text or speech from one language to another, including the contextual, idiomatic and pragmatic nuances of both languages. History of machine translation The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation.

Statistical machine translation

Statistical machine translation (SMT) was a machine translation approach, that superseded the previous, rule-based approach because it required explicit description of each and every linguistic rule, which was costly, and which often did not generalize to other languages. Since 2003, the statistical approach itself has been gradually superseded by the deep learning-based neural network approach. The first ideas of statistical machine translation were introduced by Warren Weaver in 1949, including the ideas of applying Claude Shannon's information theory.

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Related publications (32)

Automatic Speech Recognition and Translation of a Swiss German Dialect: Walliserdeutsch

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