Conversational telephone speech recognition for Lithuanian. (May 2018) - Lileikytė, Rasa; Lamel, Lori; Gauvain, Jean-Luc; Gorin, Arseniy
Highlights: Research on conversational speech recognition and keyword spotting system for the low e-resourced Lithuanian language. Grapheme-based and phoneme-based systems are studied for 2 training conditions (3 or 40 hours of transcribed audio data). The Web texts are shown to significantly improve both speech recognition and keyword spotting performance. Subword units (cross-word character and morpheme-based) are shown to improve the detection of out-of-vocabulary words. The best keywords spotting results are achieved by combining keyword hits from word and subword systems. Abstract: The research presented in the paper addresses conversational telephone speech recognition and keyword spotting for the Lithuanian language. Lithuanian can be considered a low e-resourced language as little transcribed audio data, and more generally, only limited linguistic resources are available electronically. Part of this research explores the impact of reducing the amount of linguistic knowledge and manual supervision when developing the transcription system. Since designing a pronunciation dictionary requires language-specific expertise, the need for manual supervision was assessed by comparing phonemic and graphemic units for acoustic modeling. Although the Lithuanian language is generally described in the linguistic literature with 56 phonemes, under low-resourced conditions 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.