Texas Instruments Corporation

Other

George R Doddington

Basavaraj I Pawate

Shivaling S Mahant-Shetti

Derek J Smith

Thomas Barton Schalk

Lorin Paul Netsch

Periagaram K Rajasekaran

Michael L McMahan

Panos E Papamichalis

Jayant M Naik

Richard H Wiggins, Jr

Charles T Hemphill

Warren L Bean

Craig J Cato

Bruce G Secrest

Enrico L Bocchieri

Thomas J Aton

Mark G Harward

Barbara J Wheatley

Joseph W Picone

Thomas Drew Fisher

Keith A Blanton

William M Fisher

Wallace Anderson

Masud M Arjmand

R Gary Leonard

Yeunung Chen

P K Rajasekaran

Memory chips with data memory (202), embedded logic (206) and broadcast memory (204) for two modes of operation are disclosed. A first mode of operation is the usual memory mode expected of a data RAM. The second mode of operation allows localized computation and/or processing of the data in data memory (202) by the embedded logic (206) with minimal handshaking with a remote CPU. In a functioning system, the memory chips are organized in a hierarchical manner and include address-associative memory systems.

Distributed processing memory chip with embedded logic having both data

Speaker verification is performed by computing principal components of a fixed text statement comprising a speaker identification code and a two-word phrase, and principal spectral components of a random word phrase. A multi-phrase strategy is utilized in access control to allow successive verification attempts in a single session, if the speaker fails initial attempts. Based upon a verification attempt, the system produces a verification score which is compared with a threshold value. On successive attempts, the criterion for acceptance is changed, and one of a number of criteria must be satisfied for acceptance in subsequent attempts. A speaker normalization function can also be invoked to modify the verification score of persons enrolled with the system who inherently produce scores which result in denial of access. Accuracy of the verification system is enhanced by updating the reference template which then more accurately symbolizes the person's speech signature.

Fixed text speaker verification method and apparatus

An active memory 14 is provided which includes a data memory 20 including rows and columns of storage locations for holding data and computational results. A broadcast memory 22 includes rows and columns of storage locations for holding control instructions. Computing circuitry 26 is provided which is operable to perform a first computational operation using first and second words of data retrieved from the data memory 20 and perform a second computational operation using a result from the first operation and a result from a previous operation. Control circuitry 24 is operable in response to control instructions received from broadcast memory 22 to control the transfer of the first and second words of data from the data memory 20 to said computing circuitry 26 and the performance of the first and second operations.

Apparatus, systems and methods for distributed signal processing

An active memory 14 is provided which includes a data memory 20 including rows and columns of storage locations for holding data. A broadcast memory 22 is provided which includes rows and columns of storage locations for holding control instructions. Search circuitry 26, 52 is provided which is operable to receive at least one word of data from data memory 20 and test the word against a preselected search test condition. Control circuitry 24 is operable in response to control instructions received from the broadcast memory 22 to control the transfer of the word of data from the data memory 20 to the search circuitry 26, 52 and the test of the word by the search circuitry 26, 52.

Apparatus, systems and methods for implementing memory embedded search

A memory system 10 is provided including a processor 12 and an active memory device 14 coupled to a processor 12. Active memory 14 includes a first memory 20 for storing a plurality of possible addresses and a second memory 22 for storing an actual address received from processor 12. Circuitry 26 is provided for identifying at least one active address from ones of the possible addresses stored in first memory 20 as a function of the actual address stored in second memory 22.

Devices, systems and methods for implementing a Kanerva memory

System (100) receives a speech signal at an input (102) which is measured and transformed by speech feature measuring device (104). The output feature vector from speech feature measuring device (104) is then compared to a reference model in a statistical classification manner. Acoustic similarity measuring device (106) performs statistical measurements while temporal speech model constraints block (108) imposes transitional probabilities to the probability measurements generated by measuring device (106). Acoustic similarity measuring device (106) performs a weighted analysis of the error vector defined between the speech feature vector and reference vector utilized during the analysis.

Speech analysis method and apparatus

A method and system are provided for time aligning speech. Speech data is input representing speech signals from a speaker. An orthographic transcription is input including a plurality of words transcribed from the speech signals. A sentence model is generated indicating a selected order of the words in response to the orthographic transcription. In response to the orthographic transcription, word models are generated associated with respective ones of the words. The orthographic transcription is aligned with the speech data in response to the sentence model, to the word models and to the speech data.

System and method for time aligning speech

A method and apparatus are provided for identifying one or more boundaries of a speech pattern within an input utterance. One or more anchor patterns are defined, and an input utterance is received. An anchor section of the input utterance is identified as corresponding to at least one of the anchor patterns. A boundary of the speech pattern is defined based upon the anchor section. Also provided are a method and apparatus for identifying a speech pattern within an input utterance. One or more segment patterns are defined, and an input utterance is received. Portions of the input utterance which correspond to the segment patterns are identified. One or more of the segments of the input utterance are defined responsive to the identified portions.

Apparatus and method for identifying a speech pattern

A speech system recognizes words from a spoken phrase that conform to checksum constraints. Grammar rules are applied to hypothesize words according to the checksum constraints. The checksum associated with the phrase is thus inherent in the grammar. Sentences which do not meet a predetermined checksum constraint are not valid under the grammar rules and are therefore inherently rejected. The checksum constraints result in increased recognition accuracy.

Grammar-based checksum constraints for high performance speech

An apparatus and method for verifying the identity of an unknown user of a telephone calling card service is disclosed which receives identification information corresponding to a valid user of the service, requests the unknown user to speak an authorization phrase without disclosing the authorization phrase to the unknown user, receives the unknown user's speech signal in response to the request, and verifies the identity of the unknown user based on the received speech signal. Other apparatus, systems, and methods are also disclosed.

Voice verification circuit for validating the identity of telephone

A speaker voice verification system uses temporal decorrelation linear transformation and includes a collector for receiving speech inputs from an unknown speaker claiming a specific identity, a word-level speech features calculator operable to use a temporal decorrelation linear transformation for generating word-level speech feature vectors from such speech inputs, word-level speech feature storage for storing word-level speech feature vectors known to belong to a speaker with the specific identity, a word-level speech feature vectors received from the unknown speaker with those received from the word-level speech feature storage, and speaker verification decision circuitry for determining, based on the similarity score, whether the unknown speaker's identity is the same as that claimed. The word-level vector scorer further includes concatenation circuitry as well as a word-specific orthogonalizing linear transformer. Other systems and methods are also disclosed.

Temporal decorrelation method for robust speaker verification

A speech recognizer which utilizes hypothesis testing to determine formant frequencies for use in speech recognition. A pre-processor (36) receives speech signal frames and utilizes linear predictive coding to generate all formant frequency candidates. An optimum formant selector (38) operates with a comparator (40) to select from the formant candidates those formants which best match stored reference formants. A dynamic time warper (42) and high level recognition logic (44) operate to determine whether or not to declare a recognized word.

Method for utilizing formant frequencies in speech recognition

A speaker verification system receives input speech from a speaker of unknown identity. The speech undergoes linear predictive coding (LPC) analysis and transformation to maximize separability between true speakers and impostors when compared to reference speech parameters which have been similarly transformed. The transformation incorporated a 'inter-class' covariance matrix of successful impostors within a database.

Voice verification circuit for validating the identity of an unknown

A cost-effective word recognizer. Each frame of spoken input is compared to a set of reference frames. The comparison is equivalent to embodying the reference frame as an LPC inverse filter, and is preferably done in the autocorrelation domain. To avoid the instability and computational difficulties which can be caused by a high-gain LPC inverse filter, a noise floor is introduced into each reference frame sample. Thus, for each input speech frame, a scalar measures its similarity to each of the vocabulary of reference frames. To achieve connected word recognition based on this similarity measurement, a dynamic programming algorithm is used in which time warping to match a sample to a reference is in effect permitted, and in which matching is performed with unconstrained endpoints. Thus, the word boundary decisions are made on the basis of a local maximum in similarity, and, since no separate word division decision is required, the error which can be introduced by even the best preliminary decision as to word boundaries is avoided.

Speaker-dependent connected speech word recognition method

An efficient pruning method reduces central processing unit (CPU) loading during real time speech recognition by instructing the CPU to compare a current state's previously calculated probability score against a predetermined threshold value and to discard hypothesis containing states with probability scores below such threshold. After determining that the current state should be kept, the CPU is directed to locate an available slot in the scoring buffer where information about the current state is then stored. The CPU locates an available slot by comparing the current time-index with the time-index associated with each scoring buffer slot. When they are equal, the slot is considered not available; when the current time-index is greater, the slot is considered available. After the information about the current state is stored, the CPU then sets the current state's backpointer to point at the start state of the current best path if the current states represents a completed model. Regardless of the current state's status, the CPU then associates the current time-index with the time-indices of all the slots along the best path to the current state. The CPU then proceeds to calculate the probability score of the next current state and the method repeats until all states have been completed.

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Richardson, TX, United States of America

George R Doddington