Conversational telephone speech recognition for Lithuanian

International audiencehe research presented in the paper addresses conversational telephone speechrecognition and keyword spotting for the Lithuanian language. Lithuanian can beconsidered a low e-resourced language as little transcribed audio data, and more generally,only limited linguistic resources are available electronically. Part of this research exploresthe impact of reducing the amount of linguistic knowledge and manual supervision whendeveloping the transcription system. Since designing a pronunciation dictionary requireslanguage-specific expertise, the need for manual supervision was assessed by comparingphonemic and graphemic units for acoustic modeling. Although the Lithuanian language isgenerally described in the linguistic literature with 56 phonemes, under low-resourcedconditions some phonemes may not be sufficiently observed to be modeled. Therefore different phoneme inventories were explored to assess the effects of explicitly modeling diphthongs, affricates and soft consonants. The impact of using Web data for language modeling and additional untranscribed audio data for semi-supervised training was also measured. Out-of-vocabulary (OOV) keywords are a well-known challenge for keyword search. While word-based keyword search is quite effective for in-vocabulary words, OOV keywords are largely undetected. Morpheme-based subword units are compared with character n-gram-based units for their capacity to detect OOV keywords. Experimental results are reported for two training conditions defined in the IARPA Babel program: the full language pack and the very limited language pack, for which, respectively, 40 h and 3 h of transcribed training data are available. For both conditions, grapheme-based and phoneme-based models are shown to obtain comparable transcription and keyword spotting results. The use of Web texts for language modeling is shown to significantly improve both speech recognition and keyword spotting performance. Combining full-word and subword units leads to the best keyword spotting results