ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages
Table 1: The South African Broadcast News (SABN) dataset.
['[EMPTY]', '[BOLD] Utterances', '[BOLD] Speech (h)']
As templates, we use a small corpus of isolated utterances of 40 keywords, each spoken twice by 24 South African speakers (12 male and 12 female). This set of 1920 labelled isolated keywords constitutes the only transcribed target-domain data that we use to train our keyword spotter. As untranscribed data, we use a corpus of South African Broadcast News (SABN). This 23-hour corpus consists of a mix of English newsreader speech, interviews, and crossings to reporters, broadcast between 1996 and 2006 The SABN training set is used as our untranscribed data to obtain targets for the CNN. The models then perform keyword spotting on the SABN test set. Since this set is fully transcribed, performance evaluation and analysis is possible. The isolated keywords were recorded under fairly quiet conditions and there was no speaker overlap with the SABN dataset. Hence there is a definite mismatch between the datasets. This is intentional as it reflects the intended operational setting of our system.