City name recognition over the telephone

The authors present a neural-network-based speech recognition system for telephone speech. A neural network classifier provides phoneme probabilities for each frame of the utterance. A dynamic programming algorithm finds the most probable sequence of words. The classifier was trained on a spoken name corpus which contained the test vocabulary and many other words. The test set consisted of 262 utterances containing 44 cities and 2 states. The best result obtained on the test set was 92.9% word accuracy (90.1% on just the city names). Removing phoneme duration constraints reduced recognition accuracy to 82%. Performance fell to 82.4% using a network trained on a large vocabulary, fluent-speech corpus. Several other experiments are reported which did not produce significant changes in system performance.<
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