Low-Resource-NLP

TL;DR for operators Morpho-VITS1 improves Kinyarwanda text-to-speech on both automatic and native-listener evaluation. Compared with a VITS baseline, word error rate falls from 13.4% to 9.4%, while perceived intelligibility rises from 92.1% to 96.7%. Listener scores also improve for naturalness and intonation. The architectural change is explicit linguistic structure. Kinyarwanda spelling does not mark all of the tonal information needed for natural speech, so Morpho-VITS separately represents the meaningful grammatical and lexical pieces inside words and feeds that information into phoneme-level processing. ...

Call recordings are rude. They do not arrive in clean 15-second snippets. They run for minutes or hours. Speakers interrupt each other. Background noise leaks in. Someone moves away from the microphone. Someone else speaks over music, traffic, or a ceiling fan that apparently believes it deserves co-author status. This is where many speech AI demos quietly stop being impressive. ...