Large vocabulary continuous speech recognition of Broadcast News - The Philips/RWTH approach

Abstract Automatic speech recognition of real-live broadcast news (BN) data (Hub-4) has become a challenging research topic in recent years. This paper summarizes our key efforts to build a large vocabulary continuous speech recognition system for the heterogenous BN task without inducing undesired complexity and computational resources. These key efforts included: • automatic segmentation of the audio signal into speech utterances; • efficient one-pass trigram decoding using look-ahead techniques; • optimal log-linear interpolation of a variety of acoustic and language models using discriminative model combination (DMC); • handling short-range and weak longer-range correlations in natural speech and language by the use of phrases and of distance-language models; • improving the acoustic modeling by a robust feature extraction, channel normalization, adaptation techniques as well as automatic script selection and verification. The starting point of the system development was the Philips 64k-NAB word-internal triphone trigram system. On the speaker-independent but microphone-dependent NAB-task (transcription of read newspaper texts) we obtained a word error rate of about 10%. Now, at the conclusion of the system development, we have arrived at Philips at an DMC-interpolated phrase-based crossword-pentaphone 4-gram system. This system transcribes BN data with an overall word error rate of about 17%.

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