Texas Instruments Corporation

Periagaram K Rajasekaran

George R Doddington

Lorin Paul Netsch

Carl Michael Panasik

Thomas Barton Schalk

Barbara J Wheatley

Levent M Arslan

Yeshwant K Muthusamy

Toshiaki Yoshino

Mustafa Kemal Sonmez

An improved method of providing out-of-vocabulary word rejection is achieved by adapting an initial garbage model (,) using received incoming enrollment speech (,).

Method of out of vocabulary word rejection

A network/portable computer (18) includes a modular bay (20) which can receive either a hard drive (22) or a data transceiver (24) for establishing a wireless network connection. When the network/portable computer (18) is in the local area covered by the network (10), the data transceiver (24) is installed to use the network (10) for storage and retrieval of data. When used in a remote area, where a network connection cannot be established, the hard drive (22) is installed in the modular bay (20) to provide a full featured computer.

Network computer with interchangeable hard drive and data transceiver

This invention consists of three enhancements to HMM-based automatic language identification systems. The three enhancements are: (i) language-discriminant acoustic model training and recognition, (ii) an acoustic model pruning procedure that picks only those phonetic models which are considered useful for language identification, and (iii) a neural network-based language classification method that uses knowledge-based features derived from phone sequences output by the HMM phonetic recognizers.

Automatic language identification method and system

The present invention is a speech processing system and method which includes a codebook generator (26) for generating a reference vector quantization codebook which describes a reference environment and for generating at least one secondary vector quantization codebook which describes at least one secondary environment. The secondary vector quantization codebooks are generated using the reference vector quantization codebook. A speech recognizer (38), which is trained using the reference vector quantization codebook, is also included. A pre-processor (32) accepts as input speech collected from an unknown environment and pre-processes that speech, before input to the speech recognizer (38), using the reference vector quantization codebook and an adaptation of the secondary vector quantization codebooks.

Method and system for compensating speech signals using vector

Method of encoding speech signals which is based upon determining the roots of the linear prediction polynomial describing the spectrum of an analog speech signal, wherein the roots are candidates in determining the formants of the speech signal. The method involves the analysis of respective frames of sampled digital speech data using a linear predictive technique to determine a set of reflection coefficients or K-parameters which are then converted into the equivalent predictor coefficients or A-parameters describing a prediction polynomial having a plurality of roots corresponding to the poles of an all-pole filter characterizing the vocal tract. A modified Bairstow technique is then empolyed for factoring out quadratic factors which are then sorted in an ordered arrangement in terms of ascending bandwidths. In performing the modified Bairstow technique, initial estimates of the successive quadratic factors for a current frame of digital speech data are made in sequence, and the prediction polynomial is successively deflated to a reduced order polynomial in determining the respective quadratic factors thereof. The initial estimate of the first quadratic factor is the same as the smallest bandwidth root as determined from the previous frame of digital speech data. These removed quadratic factors or roots are candidates for determining the formants of the speech signal.

Method of encoding speech signals involving the extraction of speech

Speaker-independent word recognition method and system for identifying individual spoken words based upon an acoustically distinct vocabulary of a limited number of words. The word recognition system may employ memory storage associated with a microprocessor or microcomputer in which reference templates of digital speech data representative of a limited number of words comprising the word vocabulary are stored. The word recognition system accepts an input analog speech signal from a microphone as derived from a single word-voice command spoken by any speaker. The analog speech signal is directed to an energy measuring circuit and a zero-crossing detector for determining a sequence of feature vectors based upon the zero-crossing rate and energy measurements of the sampled analog speech signal. The sequence of feature vectors are then input to the microprocessor or microcomputer for individual comparison with the feature vectors included in each of the reference templates as stored in the memory portion of the microprocessor or microcomputer. Comparison of the sequence of feature vectors as determined from the input analog speech signal with the feature vectors included in the plurality of reference templates produces a cumulative cost profile for enabling logic circuitry within the microprocessor or microcomputer to make a decision as to the identity of the spoken word. The work recognition system may be incorporated within an electronic device which is also equipped with speech synthesis capability such that the electronic device is able to recognize simple words as spoken thereto and to provide an audible comment via speech synthesis which is related to the spoken word.

Speaker-independent word recognition method and system based upon

Speaker-independent word recognition is performed, based on a small acoustically distinct vocabulary, with minimal hardware requirements. After a simple preconditioning filter, the zero crossing intervals of the input speech are measured and sorted by duration, to provide a rough measure of the frequency distribution within each input frame. The distribution of zero crossing intervals is transformed into a binary feature vector, which is compared with each reference template using a modified Hamming distance measure. A dynamic time warping algorithm is used to permit recognition of various speaker rate, and to economize on the reference template storage requirements. A mask vector for each reference template is used to ignore insignificant (or speaker-dependent) features of the words detected.

Speaker-independent word recognizer

Speaker-independent word recognition is performed, based on a small acoustically distinct vocabulary, with minimal hardware requirements. After a simple preconditioning filter, the zero crossing intervals of the input speech are measured and sorted by duration, to provide a rough measure of the frequency distribution within each input frame. The distribution of zero crossing intervals is transformed into a binary feature vector, which is compared with each reference template using a modified Hamming distance measure. A dynamic time warping algorithm is used to permit recognition of various speaker rates, and to economize on the reference template storage requirements. A mask vector with each reference vector on a template is used to ignore insignificant (or speaker-dependent) features of the words detected.

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Richardson, TX, United States of America

Periagaram K Rajasekaran