A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Speech recognition technology is evaluated on its accuracy rate, i.e. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. The vagaries of human speech have made development challenging. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction. Meanwhile, speech recognition continues to advance. Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace). Acoustics training: Attend to the acoustical side of the business.Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
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