Unsupervised Domain Discovery using Latent Dirichlet Allocation for Acoustic Modelling in Speech Recognition
Table 2: WER (%) of LDA MAP Models (K=8)
['Features', 'Model', 'RS', 'RD', 'TK', 'CT', 'MT', 'TV', 'Total']
Improvements occur across all of these domains, indicating that the LDA model can benefit all types of speech in this setup. The domains that achieved the highest gains from using LDA MAP adaptation (with PLP feature) were read speech, telephone speech and TV broadcasts, with relative WER reductions of 14%, 12%, 10% respectively compared to MAP adaptation on the manually labelled domains. The lowest gain, 4% relative, occurred on meeting speech. Similarly, with PLP+BN features telephone speech, lectures and read speech benefited the most, with relative WER reduction of 5%, 4% and 2% respectively.