Design and Optimization of a Speech Recognition Front-End for Distant-Talking Control of a Music Playback Device
Table 1: Phone Accuracy (%) for the noisy TIMIT database .
['noise model', 'mix uni.', 'mix bi.', 'babble uni.', 'babble bi.', 'music uni.', 'music bi.', 'factory uni.', 'factory bi.']
In order to provide a fair comparison, we also tuned the parameters to maximize the mean opinion score (MOS) using the Perceptual Objective Listening Quality Assessment (POLQA) , through the same GA setup and the same noisy TIMIT database. To assess the performance of our tuning method, we tested on data not used in the training by creating a second simulated noisy TIMIT database with different conditions. The SER and SNR were again chosen from uniform distributions ranging from −15 dB to −10 dB and from −10 dB to 10 dB, respectively. The “mix” noise was picked randomly from the babble, music, or factory noise. In the case of music, noisy files were generated from a set of tracks from different genres at different start points. When the front-end speech enhancer was not used, the PAR dropped to 10.1% (unigram) and 15.7% (bigram) for the noisy signal.