1. Acoustic model adaptation for telephone-based speech recognition N. Kleynhans and E. Barnard 27 January 2010

2. Introduction ASR performance degrades with training and testing data mismatch Many reasons – concentrate on environmental & channel effects Scope –High quality data acoustic models –Telephony testing data –How much adaptation is needed?

3. Method HTK –6-mixture tied-state triphone HMMs –39 MFCC (13 static, 13 Δ, 13 ΔΔ) –CMN Timit & NTimit corpora Experiments –Obtain ballpark results –Increment adaptation data (10 files at a time)

4. Results 1 of 2 TimitNTimit Timit5433 NTimit-44 Adaptation MethodAccuracy (%) MLLR (M)37 MLLR (MV)41 CMLLR46 CMLLR (*)39 Table 1. Baseline Timit and NTimit accuracies in percent. Table 2. Results for varying MLLR adaptation methods.

5. Results 2 of 2

6. Conclusion MLLR –Requires relatively small amounts of adaptation data –Effective mechanism to reduce performance degradation –Favourable over MAP