Auxiliary Interference Speaker Loss for Target-Speaker Speech Recognition
Table 1: WERs (%) for the two-speaker mixed LibriSpeech corpus in various SIR conditions. Note that for clean single-speaker speech, a clean AM achieved WERs of 4.88% and 5.54% for dev-clean and test-clean, respectively.
['SIR of the targeted speaker’s speech', 'dev-clean (two spkeakers mixed) 10', 'dev-clean (two spkeakers mixed) 5', 'dev-clean (two spkeakers mixed) 0', 'dev-clean (two spkeakers mixed) -5', 'dev-clean (two spkeakers mixed) -10', 'dev-clean (two spkeakers mixed) Avg.', 'test-clean (two spkeakers mixed) 10', 'test-clean (two spkeakers mixed) 5', 'test-clean (two spkeakers mixed) 0', 'test-clean (two spkeakers mixed) -5', 'test-clean (two spkeakers mixed) -10', 'test-clean (two spkeakers mixed) Avg.']
Note that WERs for clean speech with the clean AM was 4.88% and 5.54% for dev-clean and test-clean, respectively. As shown in the table, WERs were severely degraded by mixing the two speakers’ speech, and the clean AM produced WER averages of 83.74% and 84.71% for dev-clean and test-clean, respectively. This model dramatically improved the accuracy, achieving WER averages of 17.15% and 18.06% for dev-clean and test-clean, respectively. It achieved the best WER among all SIR conditions. The proposed auxiliary loss achieved WERs of 16.39% and 16.87% for dev-clean and test-clean, respectively; a relative WER reduction of 6.6%.