A standard ASR system is built using three types of mutually related language resources: apart from speech recordings and orthographic transcripts, a pronunciation component maps tokens in the transcripts to their phonetic representations. Its implementation is either lexicon-based (whether by way of simple lookup or of a stochastic grapheme-to-phoneme converter trained on the source lexicon) or rule-based, or a hybrid thereof. Whichever approach ends up being taken (as determined primarily by the writing system of the language in question), little attention is usually paid to pronunciation variants stemming from connected speech processes, hypoarticulation, and other phenomena typical for colloquial speech, mostly because the resource is seldom directly empirically derived. This paper presents a case study on the automatic recognition of colloquial Czech, using a pronunciation dictionary extracted from the ORTOFON corpus of informal spontaneous Czech, which is manually phonetically transcribed. The performance of the dictionary is compared to a standard rule-based pronunciation component, as evaluated against a subset of the ORTOFON corpus (multiple speakers recorded on a single compact device) and the Vystadial telephone speech corpus, for which prior benchmarks are available.
@InProceedings{LUKEŠ18.833, author = {David Lukeš and Marie Kopřivová and Zuzana Komrsková and Petra Klimešová}, title = "{Pronunciation Variants and ASR of Colloquial Speech: A Case Study on Czech}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }