Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Patent
1995-12-19
1998-12-08
Hudspeth, David R.
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
704254, G10L 506, G10L 900
Patent
active
058483888
DESCRIPTION:
BRIEF SUMMARY
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to methods and apparatus for speech recognition. Speech recognition is used as an input means for control of machines. At present, speech recognition apparatus generally recognises isolated single words. Speech recognition apparatus is also being developed which is intended to recognise multiple words spoken consecutively in a sentence or phrase; this is referred to as connected speech recognition.
2. Related Art
In speech recognition, a microphone picks up a speech signal from a speaker which is then digitised and processed for recognition. However, the microphone generally also picks up any background or ambient noise and the electrical system between the microphone and the speech recognition apparatus will likewise add noise (e.g. thermal noise, quantising noise and--where the speech is transmitted through a telecommunications channel--line noise). The noise may resemble parts of speech, for example unvoiced sibilant sounds. Accordingly, the correct recognition of a word depends strongly on the ability to distinguish the beginning and the end of the word, which correspond to the end and beginning of noise or silence. The reliability of speech recognition has been shown to depend strongly on the identification of the correct start and end points for speech.
One speech processing method which is intended to allow the recognition of a sequence of words using isolated word recognition technology as the "connected-for-isolated" (CFI) technique, described in our co-pending EP patent application 93302538.9 and incorporated herein by reference (corresponding to U.S. patent application Ser. No. 08/530,157 filed Sep. 29, 1995 as the US National Phase of PCT/BG94/00704 filed Mar. 31, 1994. This technique assumes that the signal from the microphone will include alternating periods of speech and noise, and attempts to recognise alternately speech and noise.
A common approach in speech recognition is to use statistical processing, making no initial assumptions about the mechanisms by which speech is produced. For example, hidden Markov modeling (HMM) techniques are used (as described in British Telecom Technology Journal, April 1988, vol 6 Number 2 page 105, Cox). In HMM recognition, each incoming frame of speech is compared with a number of states, to determine the likelihood of the speech frame corresponding to each of those states, and the state probabilities thus generated are compared with a number of predetermined models comprising state sequences corresponding to different words to be recognised. Whilst a word is being recognised, a number of different state sequences, and hence a number of different words, are simultaneously possible; the final determination of which state sequence was observed is made by selecting the most likely state sequence when the whole utterance is received.
Some types of HMM speech recognition maintain, during recognition, a number of possible state sequences, including a current most probable sequence for defining the word which has been recognised.
In such sequential recognisers, since the decision as to the identity of the selected word is based on the sequences of states generated, the decision cannot be made until the sequence is complete. The most likely state sequence can be recalculated for each received frame, so that as soon as the end of a word can unambiguously be identified, recognition is performed by simply outputting a recognition signal corresponding to the current most likely state sequence. The recognition process will itself produce start and end points, but this is done in conjunction with the selection of the word which is recognised and not as a separate, preliminary, end pointing step.
A CFI recogniser is therefore able to automatically locate the start and end of a word, by maintaining state sequences corresponding to noise, and recognising the sequence of noise-word-noise in the speech signal. However, many words may include gaps or stops between parts of the word, which might be m
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Johnson Stephen Howard
Power Kevin Joseph
Ringland Simon Patrick
Scahill Francis James
Talintyre John Edward
British Telecommunications PLC
Hudspeth David R.
Sax Robert Louis
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