Influence of background noise and microphone on the performance of the IBM Tangora speech recognition system

With the intention of developing a robust speech recognizer largely immune to the vagaries of extrinsic changes, the authors investigated the effects of various background noises and microphones on the performance of the Tangora system. They identified several noisy locations such as the cafeteria and a secretary's office and included a relatively quiet office for comparison. They recorded isolated-word training and test data from one male and one female speaker at different locations employing several varieties of microphones. A typical experiment consisted of designing a speaker-independent HMM (hidden Markov model) system with one set of training data and decoding the test data collected at all locations. It was found that microphone characteristics had a significant impact on the robustness of the system. It was also observed that controlled contamination of the quiet training data with ambient noise improved the noise immunity of the recognizer, discounting the role of the Lombard effect in the studies.<>