The Chancellor, Masters and Scholars of the University of Cambridge

Softsound Limited

Hewlett-packard Development Company, L.P.

Anthony John Robinson

Thomas William John Ash

Roger Cecil Ferry Tucker

Carl William Seymour

A speech processing system includes an input for receiving an input utterance spoken by a user and a word alignment unit configured to align different sequences of acoustic speech models with the input utterance spoken by the user. Each different sequence of acoustic speech models corresponds to a different possible utterance that a user might make. The system identifies any parts of a read prompt text that the user skipped; any parts of the read prompt text that the user repeated; and any speech sounds that the user inserted between words of the read prompt text. The information from the word alignment unit can be used to assess the proficiency and/or fluency of the user's speech.

Speech processing system and method

An audio signal is decomposed into lower and upper sub-band and at least the noise component of the sub-band is encoded. At the decoder the audio signal is synthesised by a decoding means which utilises a synthesised noise excitation signal and a filter to reproduce the noise component in the upper sub-band.

Audio coding systems and methods

Audio source data is subjected to a pre-emphasis step (302) to perform gross decorrelation, followed by an adaptive linear prediction (306) to perform further decorrelation. A transform is performed on the residual of the linear prediction, to obtain transform coefficients representing the residual in the frequency domain. A number of tonal components are identified (310), subtracted from the transform coefficients and encoded by vector quantization. The transform coefficients are then grouped into sub-bands, and each sub-band encoded in the frequency domain by vector quantization. The sub-bands are of uniform width on an auditory scale, so that each vector may comprise a different number of transform coefficients.

Low bit rate audio coder and decoder operating in a transform domain

In a method of automatically recognizing data which comprises sequential data units represented as sequential tokens grouped into one or more items, known items are stored as respective finite state sequence models. Each state corresponds to a token and the models which have common prefix states are organized in a tree structure such that suffix states comprise branches from common prefix states and there are a plurality of tree structures each having a different prefix state. Each sequential data unit is compared with stored reference data units identified by reference tokens to generate scores indicating the similarity of the data units to reference data units. An accumulated score for the final state in the models is determined by steps of (a) sequentially calculating the accumulated score for a model to reach the final state comprising a leaf in the tree, (b) identifying the closest branch to the leaf corresponding to a next model for which an accumulated score for the final stage has not yet been calculated, and (c) accumulating the score from the identified closest branch for the next model to the final state. These steps are repeated for the branches of the trees. The item corresponding to a model having the highest accumulated score is recognized as the model best matching the data.

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Cambridge, United Kingdom

Anthony John Robinson