Speaker model adaptation based on confidence score

Očekuje se da mjere povjerenja postanu mjera za pouzdanost rezultata sustava za prepoznavanje govora. Najčešće korištene mjere povjerenja zasnovane su na vjerojatnosti sljedeće riječi ili fonema, koja se može dobiti iz izlaznog rezultata prepoznavatelja. U ovom smo radu uveli mjeru povjerenja zasnovanu na linearnoj interpretaciji vjerojatnoće sljedeće riječi primjenom obrnute Fisher transformacije. Adaptacija govornika sastoji se od ažuriranja parametara modela nezavisnog od govornika zbog boljeg predstavljanja postojećeg govornika. Mjere povjerenja daju pouzdanije kriterije za odabir riječi koje najbolje predstavljaju govornika. Linearna interpretacija mjere povjerenja vrlo je važna pri odabiru najreprezentativnijih podataka za adaptaciju.Confidence measures are expected to give a measure of reliability on the result of a speech/speaker recognition system. Most commonly used confidence measures are based on posterior word or phoneme probabilities which can be obtained from the output of the recognizer. In this paper we introduced a linear interpretation of posterior probability based confidence measure by using inverse Fisher transformation. Speaker adaptation consists in updating model parameters of a speaker independent model to have a better representation of the current speaker. Confidence measures give more reliable selection criteria to select the utterances which best represent the speaker. A linear interpretation of confidence measure is very important to select the most representative data for adaptation

Confidence measure based Language Identification

In this paper we present a new application for confidence mea-sures in spoken language processing. In today's computerized dia-logue systems, language identification (LID) is typically achieved via dedicated modules. In our approach, LID is integrated into the speech recognizer, therefore profiting from high-level linguis-tic knowledge at very little extra cost. Our new approach is based on a word lattice based confidence measure [3], which was orig-inally devised for unsupervised training. In this work, we show that the confidence based language identification algorithm out-performs conventional score based methods. Also, this method is less dependent on the acoustic characteristics of the transmis-sion channel than score based methods. By introducing additional parameters, unknown languages can be rejected. The proposed method is compared to a score based approach on the Verbmobil database, a three language task. 1